What a Waste - A Global Review of Solid Waste Management

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KNOWLEDGE PAPERS WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT

Urban Development and Local Government Unit Sustainable Development Network The World Bank 1818 H Street, NW Washington, DC, 20433 USA Email: [email protected] Website: www.worldbank.org/urban

March 2012, No. 15

WHAT A WASTE A Global Review of Solid Waste Management

Previous Knowledge Papers in This Series Lessons and Experiences from Mainstreaming HIV/AIDS into Urban/ Water (AFTU1 & AFTU2) Projects Nina Schuler, Alicia Casalis, Sylvie Debomy, Christianna Johnnides, and Kate Kuper, September 2005, No. 1

Occupational and Environmental Health Issues of Solid Waste Management: Special Emphasis on Middle and Lower-Income Countries

Private Sector Initiatives in Slum Upgrading Judy L. Baker and Kim McClain, May 2009, No. 8

The Urban Rehabilitation of the Medinas: The World Bank Experience in the Middle East and North Africa Anthony G. Bigio and Guido Licciardi, May 2010, No. 9

Sandra Cointreau, July 2006, No. 2

Cities and Climate Change: An Urgent Agenda

A Review of Urban Development Issues in Poverty Reduction Strategies

Daniel Hoornweg, December 2010, No. 10

Judy L. Baker and Iwona Reichardt, June 2007, No. 3

Memo to the Mayor: Improving Access to Urban Land for All Residents — Fulfilling the Promise

Urban Poverty in Ethiopia: A MultiFaceted and Spatial Perspective

Barbara Lipman, with Robin Rajack, June 2011, No. 11

Elisa Muzzini, January 2008, No. 4

Urban Poverty: A Global View Judy L. Baker, January 2008, No. 5

Preparing Surveys for Urban Upgrading Interventions: Prototype Survey Instrument and User Guide Ana Goicoechea, April 2008, No. 6

Conserving the Past as a Foundation for the Future: China-World Bank Partnership on Cultural Heritage Conservation Katrinka Ebbe, Guido Licciardi and Axel Baeumler, September 2011, No. 12

Guidebook on Capital Investment Planning for Local Governments Olga Kaganova, October 2011, No. 13

Exploring Urban Growth Management: Insights from Three Cities Mila Freire, Douglas Webster, and Christopher Rose, June 2008, No. 7

Cover photo on right and on this page: Conakry landfill, Guinea (Charles Peterson photographer). Cover photo on far left: separate containers for recyclables and non-recyclables, Barcelona, Spain (Perinaz Bhada-Tata photographer).

KNOWLEDGE PAPERS

WHAT A WASTE A Global Review of Solid Waste Management

Daniel Hoornweg and Perinaz Bhada-Tata March 2012, No. 15

Urban Development Series Produced by the World Bank’s Urban Development and Local Government Unit of the Sustainable Development Network, the Urban Development Series discusses the challenge of urbanization and what it will mean for developing countries in the decades ahead. The Series aims to explore and delve more substantively into the core issues framed by the World Bank’s 2009 Urban Strategy Systems of Cities: Harnessing Urbanization for Growth and Poverty Alleviation. Across the five domains of the Urban Strategy, the Series provides a focal point for publications that seek to foster a better understanding of (i) the core elements of the city system, (ii) pro-poor policies, (iii) city economies, (iv) urban land and housing markets, (v) sustainable urban environment, and other urban issues germane to the urban development agenda for sustainable cities and communities.

Copyright © World Bank, 2012 All rights reserved

Urban Development & Local Government Unit World Bank 1818 H Street, NW Washington, DC 20433 USA www.worldbank.org/urban This publication is a product of the staff of the World Bank Group. It does not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This note is provided for information only. The World Bank has no responsibility for the persistence or accuracy of URLs and citations for external or third-party sources referred to in this publication, and does not guarantee that any content is, or will remain, accurate or appropriate.

TABLE OF CONTENTS Foreword vii Acknowledgements viii Executive Summary ix Abbreviations and Acronyms xi Country Classification According to Region xii Country Classification According to Income xiii 1. Introduction 1 2. Global Waste Management Practices 4 3. Waste Generation 8 4. Waste Collection 13 5. Waste Composition 16 6. Waste Disposal 22 7. Waste and the Environment 25 A Note on the Reliability of Solid Waste Data 32

Maxim Tupikov /Shutterstock.com

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URBAN DEVELOPMENT SERIES – KNOWLEDGE PAPERS

Annexes A. Map of Regions 36 B. Map of Income Distribution 38 C. Availability of MSW Data by Country 40 D. Countries Excluded for Lack of Data 45 E. Estimated Solid Waste Management Costs 46 F. MSW Generation Data for Cities Over 100,000 47 G. MSW Collection Data for Cities Over 100,000 63 H. MSW Disposal Methods for Cities Over 100,000 71 I. MSW Composition Data for Cities Over 100,000 78 J. MSW Generation by Country — Current Data and Projections for 2025 80 K. MSW Collection Rates by Country 84 L. MSW Disposal Methods by Country 87 M. MSW Composition by Country 90 N. IPCC Classification of MSW Composition 93 O. The Global City Indicators Program 94

References 95

WHAT A WASTE: A GLOBAL REVIEW OF SOLID WASTE MANAGEMENT

List of Tables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

Comparison of solid waste management practices by income level 5 Generators and types of solid waste 7 Current waste generation per capita by region 9 Waste generation projections for 2025 by region 10 Current waste generation per capita by income level 10 Waste generation projections for 2025 by income 11 Sources for 2025 projections of solid waste generation 12 Average MSW generation rates by income 12 Types of waste and their sources 16 Types of waste composition by income level 19 MSW disposal by income 23 MSW disposal in two contrasting regions 24 Landfill classifications 29 Landfill methane emissions and total GHG emissions for selected countries 30 Technical GHG mitigation opportunities by waste management component 31

List of Figures 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Waste generation by region 9 Waste generation by income level 11 Urban waste generation by income level and year 12 Waste collection rates by income 15 Waste collection rates by region 15 Waste composition in China 17 Global solid waste composition 17 Waste composition by income 19 Solid waste composition by income and year 20 Waste composition by region 21 Total MSW disposed of worldwide 22 Low-income countries waste disposal 24 Upper middle-income countries waste disposal 24 Waste hierarchy 27

List of Boxes 1. 2. 3. 4.

What a Waste 1999: What’s changed (and what hasn’t) in the last decade 2 Definitions of Municipal Solid Waste 4 Components of an Integrated Solid Waste Management Plan 25 Integrated Sustainable Waste Management Framework 26

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FOREWORD Solid waste management is the one thing just about every city government provides for its residents. While service levels, environmental impacts and costs vary dramatically, solid waste management is arguably the most important municipal service and serves as a prerequisite for other municipal action. Currently, world cities generate about 1.3 billion tonnes of solid waste per year. This volume is expected to increase to 2.2 billion tonnes by 2025. Waste generation rates will more than double over the next twenty years in lower income countries. Globally, solid waste management costs will increase from today’s annual $205.4 billion to about $375.5 billion in 2025. Cost increases will be most severe in low income countries (more than 5-fold increases) and lower-middle income countries (more than 4-fold increases). The global impacts of solid waste are growing fast. Solid waste is a large source of methane, a powerful GHG that is particularly impactful in the short-term. The recycling industry, with more

Ghabawi landfill, Amman, Jordan Photo: Perinaz Bhada-Tata

Photo: ©Simone D. McCourtie/World Bank

than two million informal waste pickers, is now ITC landfill and a global business with international markets and recycling center, extensive supply and transportation networks. Ankara, Turkey Locally, uncollected solid waste contributes to flooding, air pollution, and public health impacts such as respiratory ailments, diarrhea and dengue fever. In lower income country cities solid waste management is usually a city’s single largest budgetary item. The report you have before you is an important one that provides a quick snapshot of the state of today’s global solid waste management practices. A credible estimate is made for what the situation will look like in 2025. The findings are sobering. Improving solid waste management, especially in low income countries, is an urgent priority. Hopefully, this report will contribute to the dialogue that leads to much-needed action. Rachel Kyte Vice President and Head of Network, Sustainable Development The World Bank

Acknowledgements This report was written by Daniel Hoornweg and Perinaz Bhada-Tata; and managed by Abha JoshiGhani, Manager of the Urban Development and Local Government Unit and Zoubida Allaoua, Director of the Finance, Economics and Local Government Department. The ‘Waste and Climate Change’ section is from Charles Peterson. The authors would like to thank Christa Anderson, Julianne Baker Gallegos, Carl Bartone, Marcus Lee, Catalina Marulanda, John Norton, Charles Peterson, Paul Procee, and Sintana Vergara for their useful feedback and comments. The report was also discussed and reviewed by the World Bank’s Waste Management Thematic Group. Adelaide Barra, Xiaofeng Li, Jeffrey Lecksell and Claudia Lorena Trejos Gomez provided support and research assistance.

EXECUTIVE SUMMARY As the world hurtles toward its urban future, the amount of municipal solid waste (MSW), one of the most important by-products of an urban lifestyle, is growing even faster than the rate of urbanization. Ten years ago there were 2.9 billion urban residents who generated about 0.64 kg of MSW per person per day (0.68 billion tonnes per year). This report estimates that today these amounts have increased to about 3 billion residents generating 1.2 kg per person per day (1.3 billion tonnes per year). By 2025 this will likely increase to 4.3 billion urban residents generating about 1.42 kg/capita/day of municipal solid waste (2.2 billion tonnes per year). Municipal solid waste management is the most important service a city provides; in low-income countries as well as many middle-income countries, MSW is the largest single budget item for cities and one of the largest employers. Solid waste is usually the one service that falls completely


Photo: Ron Perry/Oki Golf

within the local government’s purview. A city that Golf course: cannot effectively manage its waste is rarely able post closure use to manage more complex services such as health, of landfill site education, or transportation. Poorly managed waste has an enormous impact on health, local and global environment, and economy; improperly managed waste usually results in down-stream costs higher than what it would have cost to manage the waste properly in the first place. The global nature of MSW includes its contribution to GHG emissions, e.g. the methane from the organic fraction of the waste stream, and the increasingly global linkages of products, urban practices, and the recycling industry. This report provides consolidated data on MSW generation, collection, composition, and disposal by country and by region. Despite its importance, reliable global MSW information is not typically available. Data is often inconsistent, incomparable and incomplete; however as suggested in this report there is now enough MSW information to estimate

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global amounts and trends. The report also makes projections on MSW generation and composition for 2025 in order for decision makers to prepare plans and budgets for solid waste management in the coming years. Detailed annexes provide available MSW generation, collection, composition, and disposal data by city and by country.

Men pick up used cardboard boxes to sell for recycling in the San Joaquin open-air market in Salvador, Brazil

Globally, waste volumes are increasing quickly – even faster than the rate of urbanization. Similar to rates of urbanization and increases in GDP, rates of MSW growth are fastest in China, other parts of East Asia, and parts of Eastern Europe and the Middle East. Municipal planners should manage solid waste in as holistic a manner as possible. There is a strong correlation between urban solid waste generation rates and GHG emissions. This link is likely similar with other urban inputs/ outputs such as waste water and total energy use. Reviewing MSW in an integrated manner with a more holistic approach, focusing on urban form and lifestyle choice may yield broader benefits.

Pollution such as solid waste, GHG emissions and ozone-depleting substances are by-products of urbanization and increasing affluence. Improving MSW is one of the most effective ways to strengthen overall municipal management and is usually a prerequisite for other, more complicated, municipal services. Waste workers, both formal and informal, have a significant impact on overall MSW programming. While in more affluent countries ageing workers are a growing challenge, the effective integration of waste pickers, particularly in low-income countries, is critical. This report is a follow-up to What a Waste: Solid Waste Management in Asia, a Working Paper Published by the East Asia and the Pacific Region Urban and Local Government Sector of the World Bank in 1999. The report has been expanded to include the entire world, given data availability and increased inter-dependence between nations and linkages in global trade, particularly that of secondary materials. Photo: Alejandro Lipszyc/World Bank


Abbreviations and Acronyms AFR

Africa region

C&D

Construction and demolition

CDM

Clean Development Mechanism

EAP

East Asia and Pacific region

ECA

Europe and Central Asia region

GDP

Gross Domestic Product

GHG

Greenhouse gas

HIC

High-income country

ICI

Industrial, commercial, and institutional

IPCC

Intergovernmental Panel on Climate Change

ISWM

Integrated solid waste management

Kg/capita/day kilograms per capita per day LCR

Latin America and the Caribbean region

LIC

Low-income country

LMIC

Lower middle-income country

MENA

Middle East and North Africa region

METAP

Mediterranean Environmental Technical Assistance Program

MRF

Materials recovery facility

MSW

Municipal solid waste

mtCO2e

Million tonnes of carbon dioxide equivalent

OECD

Organisation for Economic Co-operation and Development

PAHO

Pan-American Health Organization

RDF

Refuse–derived fuel

SAR

South Asia region

SWM

Solid waste management

tCO2e

Tons of carbon dioxide equivalent

UMIC

Upper middle-income country

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Country Classification According to Region East Asia & Pacific (EAP)

Africa (AFR)

Eastern & Central Asia (ECA)

Latin America & the Caribbean (LAC)

Angola

Brunei Darussalam

Albania

Antigua and Barbuda

Benin

Cambodia

Armenia

Argentina

Botswana

China

Belarus

Bahamas, The

Burkina Faso

Fiji

Bulgaria

Barbados

Burundi

Hong Kong

Croatia

Belize

Cameroon

Indonesia

Cyprus

Cape Verde

Lao PDR

Central African Republic

Macao, China

Chad Comoros Congo, Dem. Rep.

Mongolia

Congo, Rep.

Myanmar

Cote d’Ivoire

Philippines

Romania

Middle East & North Africa (MENA) Algeria

Organisation for Economic Co-operation and Development (OECD) Andorra

Bangladesh

Bahrain

Australia

Bhutan

Egypt, Arab Rep.

Austria

India

Iran, Islamic Rep.

Belgium

Maldives

Iraq

Canada

Nepal

Bolivia

Israel

Czech Republic

Pakistan

Estonia

Brazil

Jordan

Denmark

Sri Lanka

Georgia

Chile

Kuwait

Finland

Malaysia

Latvia

Colombia

Lebanon

France

Marshall Islands

Lithuania

Costa Rica

Malta

Germany

Macedonia, FYR

Cuba

Morocco

Greece

Poland

Dominica

Oman

Hungary

Dominican Republic

Qatar

Iceland

Eritrea

Singapore

Russian Federation

Ecuador

Saudi Arabia

Ireland

Ethiopia

Solomon Islands

Serbia

El Salvador

Syrian Arab Republic

Italy

Gabon

Thailand

Slovenia

Grenada

Tunisia

Japan

Gambia

Tonga

Tajikistan

Guatemala

United Arab Emirates

Korea, South

West Bank and Gaza

Luxembourg

Ghana

Vanuatu

Turkey

Guyana

Guinea

Vietnam

Turkmenistan

Haiti

Monaco

Kenya

Honduras

Netherlands

Lesotho

Jamaica

New Zealand

Liberia

Mexico

Norway

Madagascar

Nicaragua

Portugal

Malawi

Panama

Slovak Republic

Mali

Paraguay

Spain

Mauritania

Peru

Sweden

Mauritius

St. Kitts and Nevis

Switzerland

Mozambique

St. Lucia

United Kingdom

Namibia

St. Vincent and the Grenadines

United States

Niger

Suriname

Nigeria

Trinidad and Tobago

Rwanda

Uruguay

Sao Tome and Principe

Venezuela, RB

Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe

South Asia (SAR)


Country Classification According to Income Lower Income (LI)

Lower Middle Income (LMI)

Upper Middle Income (UMI)

High Income (HIC)

Chad

Bulgaria

Colombia

Barbados

Comoros

Cameroon

Costa Rica

Belgium

Congo, Dem. Rep.

Cape Verde

Cuba

Brunei Darussalam

Eritrea

China

Dominica

Canada

Ethiopia

Congo, Rep.

Dominican Republic

Croatia

Gambia

Cote d'Ivoire

Fiji

Cyprus

Ghana

Ecuador

Gabon

Czech Republic

Guinea

Egypt, Arab Rep.

Georgia

Denmark

Haiti

El Salvador

Grenada

Estonia

Kenya

Guatemala

Jamaica

Finland

Lao PDR

Guyana

Latvia

France

Liberia

Honduras

Lebanon

Germany

Madagascar

India

Lithuania

Greece

Malawi

Indonesia

Malaysia

Hong Kong, China

Mali

Iran, Islamic Rep.

Mauritius

Hungary

Mauritania

Iraq

Mexico

Iceland

Mongolia

Jordan

Myanmar

Ireland

Mozambique

Lesotho

Namibia

Israel

Nepal

Macedonia, FYR

Panama

Italy

Niger

Maldives

Peru

Japan

Rwanda

Marshall Islands

Poland

Korea, South

Senegal

Morocco

Romania

Kuwait

Serbia

Nicaragua

Russian Federation

Luxembourg

Sierra Leone

Nigeria

Seychelles

Macao, China

Tanzania

Pakistan

South Africa

Malta

Togo

Paraguay

St. Kitts and Nevis

Monaco

Uganda

Philippines

St. Lucia

Netherlands

Vanuatu

Sao Tome and Principe


New Zealand

Vietnam

Solomon Islands

Suriname

Norway

Zambia

Sri Lanka

Tajikistan

Oman

Zimbabwe

Sudan

Uruguay

Portugal

Swaziland

Venezuela, RB

Qatar

Syrian Arab Republic

Saudi Arabia

Thailand

Singapore

Tonga

Slovak Republic

Tunisia

Slovenia

Turkey

Spain

Turkmenistan

Sweden

West Bank and Gaza

Switzerland Trinidad and Tobago United Arab Emirates United Kingdom United States

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Introduction In 1999 the World Bank published What a Waste: Solid Waste Management in Asia (Hoornweg and Thomas 1999), with an estimate of waste quantities and composition for Asia. In the intervening decade more accurate and comprehensive data became available for most regions of the world. OECD-country estimates are typically reliable and consistent—added to these were comprehensive studies for China and India and the Pan-American Health Organization’s study for Latin America. Therefore a global update of the 1999 report is possible, and timely. Municipal solid waste managers are charged with an enormous task: get the waste out from underfoot and do so in the most economically, socially, and environmentally optimal manner possible. Solid waste management is almost always the responsibility of local governments and is often their single largest budget item, particularly in developing countries. Solid waste management and street sweeping is also often the city’s single largest source of employment.1 Additionally, solid waste is one of the most pernicious local pollutants — uncollected solid waste is usually the leading contributor to local flooding and air and water pollution. And if that task were not large enough, local waste management officials also need to deal with the integrated and international aspects of solid waste, and increasingly with demographic change in the work force, employment generation, and management of staff — both formal and informal.

Managing municipal solid waste is an intensive service. Municipalities need capacities in procurement, contract management, professional and often unionized labor management, and ongoing expertise in capital and operating budgeting and finance. MSW also requires a strong social contract between the municipality and community. All of these skills are prerequisites for other municipal services. The original What a Waste Report provided waste estimates for South and East Asia. This waste stream represents about 33% of the world’s total quantities. Most growth predictions made in What a Waste: Solid Waste Management in Asia were reasonably accurate and in most cases, even taking into account the recent economic contraction, waste growth estimates were conservative. This is especially true in China. In 2004, China surpassed the US as the world’s largest waste generator. In 2030, China will likely produce twice as much municipal solid waste as the United States. The main objective of this updated What a Waste Report is to provide current municipal solid waste

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Solid waste management — formal and informal – represents 1% to 5% of all urban employment. As formality increases so do issues of labor organization, health and safety, ageing demographics (solid waste workers tend to be younger), the friction between ‘sanctioned’ and ‘unsanctioned’ recycling, and producer pay arguments and apportioning costs and responsibilities.

Ferry men parking their boats on Buriganga River, Dhaka. Photo taken as part of Development 360 project. Photo: Scott Wallace Illustration: Brian Fray

BOX 1

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What a Waste 1999: What’s Changed (and What Hasn’t) in the Last Decade

``What a Waste (1999) predicted that by 2025 the daily MSW generation rate in Asia would be 1.8 million tonnes per day. These estimates are still accurate. At present, the daily generation rate in South Asia and East Asia and the Pacific combined is approximately 1 million tonnes per day.

``Low-income countries continue to spend most of their SWM budgets on waste collection, with only a fraction going toward disposal. This is the opposite in high-income countries where the main expenditure is on disposal.

`Àsia, like much of the world, continues to have a majority of organics and paper in its waste stream: The combined totals are 72% for EAP and 54% for SAR. Growth in waste quantities is fastest in Asia.

``There is a greater emphasis on labor issues: in highincome countries, demographics and immigration are critical factors; in low-income countries working conditions and integration of waste pickers has gained in importance.

``Rates of recycling are increasingly influenced by global markets, relative shipping costs, and commodity prices.

Lisbon, Portugal, used aluminum cans are deposited into a container for recycling Bigstock Photo

©

generation, composition, collection, and disposal data by country and by region. Both developing and developed countries are included. This report makes projections on MSW generation and composition on a country and regional level for 2025. This should provide decision makers with a sufficient foundation on which to base waste management policy decisions. In most cases further local analysis will be needed, but this report is intended to provide a broad global review. For a summary on the main differences between the data presented in the 1999 publication and this publication, please refer to Box 1. Solid waste is inextricably linked to urbanization and economic development. As countries

urbanize, their economic wealth increases. As standards of living and disposable incomes increase, consumption of goods and services increases, which results in a corresponding increase in the amount of waste generated. This report estimates that at present almost 1.3 billion tonnes of MSW are generated globally every year, or 1.2 kg/capita/day. The actual per capita rates, however, are highly variable, as there are considerable differences in waste generation rates across countries, between cities, and even within cities. Solid waste is generally considered an ‘urban’ issue. Waste generation rates tend to be much lower in rural areas since, on average, residents are usually poorer, purchase fewer store-bought


items (which results in less packaging), and have higher levels of reuse and recycling. Today, more than 50 percent of the world’s population lives in cities, and the rate of urbanization is increasing quickly. By 2050, as many people will live in cities as the population of the whole world in 2000. This will add challenges to waste disposal. Citizens and corporations will likely need to assume more responsibility for waste generation and disposal, specifically, product design and waste separation. Also likely to emerge will be a greater emphasis on ‘urban mining’ as the largest source of materials like metal and paper may be found in cities. Waste is mainly a by-product of consumer-based lifestyles that drive much of the world’s economies. In most cities, the quickest way to reduce waste volumes is to reduce economic activity—not

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generally an attractive option. Solid waste is the most visible and pernicious by-product of a resource-intensive, consumer-based economic lifestyle. Greenhouse gas emissions, water pollution and endocrine disruptors are similar by-products to our urban lifestyles. The long term sustainability of today’s global economic structure is beyond the scope of this paper. However, solid waste managers need to appreciate the global context of solid waste and its interconnections to economies and local and global pollution. This report makes projections for MSW generation in 2025, based on expected population and economic growth rates. As countries, particularly India and China, continue their rapid pace of urbanization and development, global solid waste quantities are projected to increase considerably.

Illustration: Brian Fray

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Global Waste Management Practices At a Glance: ` In solid waste management there is no throwing ‘away’. ` The organic fraction of waste, collection vehicles, and waste disposal methods contribute to GHG emissions. ` The last two decades have brought a new challenge for waste management: the growing vagaries of global secondary materials markets.

containerized to stay dry, and much of the waste stream is not combustible. Landfills require land availability, and siting is often opposed by potential neighboring residents. Solving one problem often introduces a new one, and if not well executed, the new problem is often of greater cost and complexity.

BOX 2

In solid waste management there is no ‘away’. When ‘throwing away’ waste, system complexities and the integrated nature of materials and pollution are quickly apparent. For example, waste incineration is expensive and poses challenges of air pollution and ash disposal. Incineration requires waste placed outside for collection to be

Definitions of Municipal Solid Waste

By PAHO: Solid or semi-solid waste generated in population centers including domestic and, commercial wastes, as well as those originated by the small-scale industries and institutions (including hospital and clinics); market street sweeping, and from public cleansing. By IPCC: The IPCC includes the following in MSW: food waste; garden (yard) and park waste; paper and cardboard; wood; textiles; nappies (disposable diapers); rubber and leather; plastics; metal; glass (and pottery and china); and other (e.g., ash, dirt, dust, soil, electronic waste).

ITC landfill and recycling center, Ankara, Turkey

Photo: ©Simone D. McCourtie/World Bank

By OECD: Municipal waste is collected and treated by, or for municipalities. It covers waste from households, including bulky waste, similar waste from commerce and trade, office buildings, institutions and small businesses, yard and garden, street sweepings, contents of litter containers, and market cleansing. Waste from municipal sewage networks and treatment, as well as municipal construction and demolition is excluded.


TABLE 1

Comparison of Solid Waste Management Practices by Income Level (adapted from What a Waste 1999) Activity

Low Income

Middle Income

High Income

Source Reduction No organized programs, but reuse and low per capita waste generation rates are common.

Some discussion of source reduction, but rarely incorporated into an organized program.

Organized education programs emphasize the three ‘R’s’ — reduce, reuse, and recycle. More producer responsibility & focus on product design.

Collection

Sporadic and inefficient. Service is limited to high visibility areas, the wealthy, and businesses willing to pay. High fraction of inerts and compostables impact collection—overall collection below 50%.

Improved service and increased collection from residential areas. Larger vehicle fleet and more mechanization. Collection rate varies between 50 to 80%. Transfer stations are slowly incorporated into the SWM system.

Collection rate greater than 90%. Compactor trucks and highly mechanized vehicles and transfer stations are common. Waste volume a key consideration. Aging collection workers often a consideration in system design.

Recycling

Although most recycling is through the informal sector and waste picking, recycling rates tend to be high both for local markets and for international markets and imports of materials for recycling, including hazardous goods such as e-waste and ship-breaking. Recycling markets are unregulated and include a number of ‘middlemen’. Large price fluctuations.

Informal sector still involved; some high technology sorting and processing facilities. Recycling rates are still relatively high. Materials are often imported for recycling. Recycling markets are somewhat more regulated. Material prices fluctuate considerably.

Recyclable material collection services and high technology sorting and processing facilities are common and regulated. Increasing attention towards long-term markets.

Composting

Rarely undertaken formally even though the waste stream has a high percentage of organic material. Markets for, and awareness of, compost lacking.

Large composting plants are often unsuccessful due to contamination and operating costs (little waste separation); some small-scale composting projects at the community/ neighborhood level are more sustainable. Composting eligible for CDM projects but is not widespread. Increasing use of anaerobic digestion.

Becoming more popular at both backyard and large-scale facilities. Waste stream has a smaller portion of compostables than low- and middle-income countries. More source segregation makes composting easier. Anaerobic digestion increasing in popularity. Odor control critical.

Incineration

Not common, and generally not successful because of high capital, technical, and operation costs, high moisture content in the waste, and high percentage of inerts.

Some incinerators are used, but experiencing financial and operational difficulties. Air pollution control equipment is not advanced and often by-passed. Little or no stack emissions monitoring. Governments include incineration as a possible waste disposal option but costs prohibitive. Facilities often driven by subsidies from OECD countries on behalf of equipment suppliers.

Prevalent in areas with high land costs and low availability of land (e.g., islands). Most incinerators have some form of environmental controls and some type of energy recovery system. Governments regulate and monitor emissions. About three (or more) times the cost of landfilling per tonne.

Landfilling/ Dumping

Low-technology sites usually open dumping of wastes. High polluting to nearby aquifers, water bodies, settlements. Often receive medical waste. Waste regularly burned. Significant health impacts on local residents and workers.

Some controlled and sanitary landfills with some environmental controls. Open dumping is still common. CDM projects for landfill gas are more common.

Sanitary landfills with a combination of liners, leak detection, leachate collection systems, and gas collection and treatment systems. Often problematic to open new landfills due to concerns of neighboring residents. Post closure use of sites increasingly important, e.g. golf courses and parks.

Costs (see Annex E)

Collection costs represent 80 to 90% of the municipal solid waste management budget. Waste fees are regulated by some local governments, but the fee collection system is inefficient. Only a small proportion of budget is allocated toward disposal.

Collection costs represent 50% to 80% of the municipal solid waste management budget. Waste fees are regulated by some local and national governments, more innovation in fee collection, e.g. included in electricity or water bills. Expenditures on more mechanized collection fleets and disposal are higher than in low-income countries.

Collection costs can represent less than 10% of the budget. Large budget allocations to intermediate waste treatment facilities. Up front community participation reduces costs and increases options available to waste planners (e.g., recycling and composting).

Overall recycling rates higher than low and middle income. Informal recycling still exists (e.g. aluminum can collection.) Extended product responsibility common.

5

6


Locally, waste collection vehicles are large sources of emissions and both incineration and landfilling contribute GHG emissions. Uncollected waste can provide breeding areas and food to potentially disease carrying vectors such as insects and rodents, with their associated health and nuisance issues. Waste management cannot be effectively managed without due consideration for issues such as the city’s overall GHG emissions, labor market, land use planning, and myriad related concerns. Despite progress in solid waste management practices in the decade since the original What a Waste Report was published, fundamental institutional, financial, social, and environmental problems still exist. Although each country and city has their own site-specific situations, general observations can be made across low-, middle-, and high-income countries, as delineated in Table 1. The average city’s municipal waste stream is made up of millions of separate waste items. For a compilation of the different definitions for Municipal Solid Waste, please refer to Box 2. In many cases, items in a city’s waste stream originated from other countries that have countless factories and independent producers. Some of the larger waste fractions, such as organics (food and horticultural waste) and paper are easier to manage, but wastes such as multi-laminates, hazardous (e.g. syringes), and e-waste, pose disproportionately large problems. Industry programs, such as voluntary plastic-type labeling, are largely ineffective (no facilities exist to differentiate containers by numbers, either mechanically or by waste-worker) and deposit-return systems often meet industry and consumer resistance. Hybrid, ad hoc, and voluntary take-back programs are emerging, however they are generally inefficient

and municipalities are often forced to subsidize the disposal costs of these items. In the last ten to twenty years an additional challenge has emerged for the waste manager: the growing global vagaries of secondary materials markets. Many municipal recycling programs in Europe and North America were started with the recycling markets relatively close to source. More recently, marketing of secondary-materials has emerged as a global business. The price paid per tonne of waste paper in New York City is often based on what the purchase price is in China. The majority of waste recycled in Buenos Aires, for example, is shipped to China. The volatility of secondary materials prices has increased, making planning more difficult. The price is often predictive of economic trends, dropping significantly during economic downturns (when a city is least able to afford price drops). There are some hedging opportunities for materials pricing, however secondary materials marketing does not have the same degree of sophistication as other commodities (largely due to issues of reliability, quality, externalities, and the sheer number of interested parties). In the years that have passed since the original What a Waste report was released, two comprehensive World Bank studies on India and China have been prepared (Hanrahan et al 2006 and Hoornweg et al 2005). Additionally, OECD and PAHO have released MSW data for Latin America and the Caribbean. This version of What a Waste includes the data presented by these reports. MSW, as defined in this report, encompasses residential, industrial, commercial, institutional, municipal, and construction and demolition (C&D) waste. Table 2 gives sources and types of waste generated.


Source

Typical Waste Generators

Types of Solid Wastes

Residential

Single and multifamily dwellings

Food wastes, paper, cardboard, plastics, textiles, leather, yard wastes, wood, glass, metals, ashes, special wastes (e.g., bulky items, consumer electronics, white goods, batteries, oil, tires), and household hazardous wastes (e.g., paints, aerosols, gas tanks, waste containing mercury, motor oil, cleaning agents), e-wastes (e.g., computers, phones, TVs)

Industrial

Light and heavy manufacturing, fabrication, construction sites, power and chemical plants (excluding specific process wastes if the municipality does not oversee their collection)

Housekeeping wastes, packaging, food wastes, construction and demolition materials, hazardous wastes, ashes, special wastes

Commercial

Stores, hotels, restaurants, markets, office buildings

Paper, cardboard, plastics, wood, food wastes, glass, metals, special wastes, hazardous wastes, e-wastes

Institutional

Schools, hospitals (non-medical waste), prisons, government buildings, airports

Same as commercial

Construction and Demolition

New construction sites, road repair, renovation sites, demolition of buildings

Wood, steel, concrete, dirt, bricks, tiles

Municipal Services

Street cleaning, landscaping, parks, beaches, other recreational areas, water and wastewater treatment plants

Street sweepings; landscape and tree trimmings; general wastes from parks, beaches, and other recreational areas, sludge

All of the above should be included as municipal solid waste. Industrial, commercial, and institutional (ICI) wastes are often grouped together and usually represent more than 50% of MSW. C&D waste is often treated separately: if well managed it can be disposed separately. The items below are usually considered MSW if the municipality oversees their collection and disposal. Process

Heavy and light manufacturing, refineries, chemical plants, power plants, mineral extraction and processing

Industrial process wastes, scrap materials, off-specification products, slag, tailings

Medical waste

Hospitals, nursing homes, clinics

Infectious wastes (bandages, gloves, cultures, swabs, blood and body fluids), hazardous wastes (sharps, instruments, chemicals), radioactive waste from cancer therapies, pharmaceutical waste

Agricultural

Crops, orchards, vineyards, dairies, feedlots, farms

Spoiled food wastes, agricultural wastes (e.g., rice husks, cotton stalks, coconut shells, coffee waste), hazardous wastes (e.g., pesticides)

7

TABLE 2

Generators and Types of Solid Waste (adapted from What a Waste 1999)

8


Waste Generation At a Glance: ` MSW generation levels are expected to double by 2025. ` The higher the income level and rate of urbanization, the greater the amount of solid waste produced. ` OECD countries produce almost half of the world’s waste, while Africa and South Asia regions produce the least waste.

and as disposable incomes and living standards increase, consumption of goods and services correspondingly increases, as does the amount of waste generated. Urban residents produce about twice as much waste as their rural counterparts.

Current global MSW generation levels are approximately 1.3 billion tonnes per year, and are expected to increase to approximately 2.2 billion tonnes per year by 2025. This represents a significant increase in per capita waste generation rates, from 1.2 to 1.42 kg per person per day in the next fifteen years. However, global averages are broad estimates only as rates vary considerably by region, country, city, and even within cities.

Waste Generation by Region Waste generation varies as a function of affluence, however, regional and country variations can be significant, as can generation rates within the same city. Annex A. Map of Regions illustrates the regional classification used in this report. Throughout the report, when Africa is mentioned as a region, we refer to Sub-Saharan Africa. Data are particularly lacking for Sub-Saharan Africa.

MSW generation rates are influenced by economic development, the degree of industrialization, public habits, and local climate. Generally, the higher the Collecting paper economic development and rate of urbanization, to be recycled, the greater the amount of solid waste produced. Mumbai, India Income level and urbanization are highly correlated

Waste generation in sub-Saharan Africa is approximately 62 million tonnes per year. Per capita waste generation is generally low in this region, but spans a wide range, from 0.09 to 3.0 kg per person per day, with an average of 0.65 kg/capita/day. The countries with the highest per capita rates are islands, likely due to waste generated by the tourism industry, and a more complete accounting of all wastes generated. The annual waste generation in East Asia and the Pacific Region is approximately 270 million tonnes per year. This quantity is mainly influenced by waste generation in China, which makes up 70% of the regional total. Per capita waste generation ranges from 0.44 to 4.3 kg per person per day for Photo: Jeroo Bhada


TABLE 3

Waste Generation Per Capita (kg/capita/day) Region

Lower Boundary

Upper Boundary

Average

AFR

0.09

3.0

0.65

EAP

0.44

4.3

0.95

ECA

0.29

2.1

1.1

LAC

0.11

MENA

0.16

14

5.7

1.1

OECD

1.10

3.7

2.2

0.12

5.1

0.45

In Eastern and Central Asia, the waste generated per year is at least 93 million tonnes. Eight countries in this region have no available data on waste generation in the literature. The per capita waste generation ranges from 0.29 to 2.1 kg per person per day, with an average of 1.1 kg/capita/day. Latin America and the Caribbean has the most comprehensive and consistent data (e.g. PAHO’s Regional Evaluation of Solid Waste Management, 2005). The total amount of waste generated per year in this region is 160 million tonnes, with per capita values ranging from 0.1 to 14 kg/capita/ day, and an average of 1.1 kg/capita/day. Similar to the high per capita waste generation rates on islands in Africa, the largest per capita solid waste generation rates are found in the islands of the Caribbean. In the Middle East and North Africa, solid waste generation is 63 million tonnes per year. Per capita waste generation is 0.16 to 5.7 kg per person per day, and has an average of 1.1 kg/capita/day. The OECD countries generate 572 million tonnes of solid waste per year. The per capita values range from 1.1 to 3.7 kg per person per day with an average of 2.2 kg/capita/day.

Current Waste Generation Per Capita by Region (see Annex J)

1.1

2

SAR

the region, with an average of 0.95 kg/capita/day (Hoornweg et al 2005).

9

In South Asia, approximately 70 million tonnes of waste is generated per year, with per capita values ranging from 0.12 to 5.1 kg per person per day and an average of 0.45 kg/capita/day. Table 3 shows current waste generation per capita by region, indicating the lower boundary and upper boundary for each region, as well as average kg per capita per day of waste generated within each region.2 Figure 1 illustrates global waste generation per region, where OECD countries make up almost half Figure 1. Current Waste Generation by Region

SAR 5%

AFR 5%

FIG. 1

MENA 6%

Waste Generation by Region

ECA 7% OECD 44%

LAC 12%

EAP 21%

10


TABLE 4

Current Available Data

Waste Generation Projections for 2025 by Region

Total Urban Population (millions)

Region

Projections for 2025

Urban Waste Generation Per Capita (kg/capita/day)

Total (tons/day)

Total Population (millions)

Per Capita (kg/capita/day)

Total (tons/day)

AFR

260

0.65

169,119

1,152

518

0.85

441,840

777

0.95

738,958

2,124

1,229

1.5

1,865,379

ECA

227

1.1

254,389

339

239

1.5

354.810

LCR

399

1.1

437,545

681

466

1.6

728,392

MENA

162

1.1

173,545

379

257

1.43

369,320

1,566,286

1,031

842

2.1

1,742,417

192,410

1,938

734

0.77

567,545

3,532,252

7,644

4,285

OECD

729

2.2

SAR

426

0.45

2,980

1.2

TABLE 5 Income Level

1.4

6,069,703

Waste Generation Per Capita (kg/capita/day) Lower Boundary

Upper Boundary

Average

High

0.70

14

2.1

Upper Middle

0.11

5.5

1.2

Lower Middle

0.16

5.3

0.79

Lower

0.09

4.3

0.60

of the world’s waste, while Africa and South Asia figure as the regions that produce the least waste. Table 4 shows estimates of waste generation for the year 2025 as expected according to current trends in population growth in each region.

Waste Generation by Country Income Level 3 High-income countries produce the most waste per capita, while low income countries produce the least solid waste per capita. Although the total waste generation for lower middle income countries is higher than that of upper middle income countries, likely skewed as a result of China’s inclusion in the lower middle income 3

Urban Population (millions)

Projected Urban Waste

EAP

Total

Current Waste Generation Per Capita by Income Level

Projected Population

Countries are classified into four income levels according to World Bank estimates of 2005 GNI per capita. High: $10,726 or above; Upper middle: $3,466-10,725; Lower middle: $876-3,465; and Lower: $875 or less.

group, the average per capita waste generation amounts for the various income groups reflect the income level of the countries (see Figure 2). The high, upper-middle, lower-middle, and low income designations are somewhat inaccurate as these classifications are country-wide, and in several countries average national affluence can be very different from average affluence of the urban populations. Only the affluence of urban residents is important in projecting MSW rates. For example, India and especially China have disproportionately high urban waste generation rates per capita relative to overall economic status as they have large relatively poor rural populations that tend to dilute national figures. Annex B. Map of Income Distribution illustrates the global classification for income used in this report. Table 5 shows current waste generation per capita by income level, indicating the lower


11

Figure 2. Waste Generation by Country Income

boundary and upper boundary for each region, as well as average kg per capita per day of waste generated within each group according to country income level. Figure 2 presents global waste generation by country per income level, showing decreasing average rates of per capita waste generation according to income level.

Lower Income 6%

FIG. 2

Waste Generation by Income

Lower Middle Income 29%

High Income 46%

Table 6 shows estimates of waste generation for the year 2025 as expected according to current trends in population growth as determined by country income level.

Methodology for collecting current data: MSW generation data by country were collected from official government publications, reports by international agencies, and articles in peerreviewed journals. Where possible, this report has used the same source for a group of countries so that the data are relatively standardized by methodology and year. For example, MSW generation data for high-income countries are from OECD publications; countries in Latin America and the Caribbean from PAHO studies; and some Middle Eastern countries from METAP data. In cases where only per capita waste generation rates were available, the total urban population for that year (World Bank, World Development Indicators) was used to calculate the total urban MSW generation.

Upper Middle Income 19%

Where only total MSW generation numbers were available, total urban population for that year was used to calculate per capita waste generation, assuming that most of the waste generated is in urban areas and only a small fraction comes from rural areas. For several African countries, data were not readily available. Hence, a per capita amount of 0.5 kg/ capita/day is assumed for urban areas for 2005. This estimate is based on the USAID 2009 publication on Environmental Guidelines for Small-Scale Activities in Africa (EGSSAA), 2nd Ed. and World Bank studies. For further information on MSW generation rates by country, please see Annex J. When reviewing

Current Available Data Region


TABLE 6

Projections for 2025 (from Annex J)

Urban Waste Generation


Per Capita (kg/capita/ day)

Total (tons/day)

343

0.60

204,802

1,637

676

Lower Middle Income

1,293

0.78

1,012,321

4,010

2,080

1.3

2,618,804

Upper Middle Income

572

1.16

665,586

888

619

1.6

987,039

774

2.13

1,649,547

1,112

912

2.1

1,879,590

2,982

1.19

3,532,256

7,647

4,287

1.4

6,069,705

Lower Income

High Income Total



Projected Urban Waste Per Capita (kg/capita/ day)

Total (tons/day)

0.86

584,272

Waste Generation Projections for 2025 by Income

12


TABLE 7

Sources for 2025 Projections of Solid Waste Generation

Variable Current GDP (current US$, 2005)

World Development Indicators

GDP Projections by Region

IEA Annual Energy Outlook (2005)

Urban Population Projections

United Nations World Urbanization Prospects (2007)

TABLE 8

Average MSW Generation Rates by Income

Data Source

Income Level

Average MSW Generation (kg/cap/day)

Low-Income

0.6 – 1.0

Middle-Income

0.8 – 1.5

High-Income

1.1 – 4.5

GDP (high-, middle-, or low-income) and an average range of MSW generation based on that income level. Modest adjustments for current experience and waste generation practices were made where appropriate. Similar to ‘energy intensity’ urban residents also exhibit ‘waste intensity’.

the values presented in this report, it’s important to keep in mind that values for waste generation at a regional level can differ markedly because of the influence from a single country, such as the US, China or India.

Methodology for calculating 2025 projections:

For further information on the sources used for the 2025 projections please refer to Table 7. Projections for urban municipal solid waste generation in 2025 were made by factoring expected Figure 3. Urban Waste Generation Table 8 illustrates the range of MSW based on growth in population and GDP and estimated country income level. These values are supported per capita waste generation. Projections for each by Table 6. country were made based on the level of expected

FIG. 3

Urban Waste Generation by Income Level and Year

Waste Generated (millions tons/year)

1,200 956

1,000 800

686 602

600 369

400

243

213

200 0

Urban Population (millions) Waste (kg/capita/year) Country Income Group

360

75 343 219

676 343

Lower Income

1,293 2,080 288 344

573 423

619 628

Lower Middle Income

Upper Middle Income

2010

Projected 2025

774 777

912 840

High Income


13

Waste Collection At a Glance: ` MSW collection is an important aspect in maintaining public health in cities around the world. ` The amount of MSW collected varies widely by region and income level; collection within cities can also differ greatly. ` Collection rates range from a low of 41% in low-income countries to a high of 98% in high-income countries.

Waste collection is the collection of solid waste from point of production (residential, industrial commercial, institutional) to the point of treatment or disposal. Municipal solid waste is collected in several ways: 1. House-to-House: Waste collectors visit each individual house to collect garbage. The user generally pays a fee for this service. 2. Community Bins: Users bring their garbage to community bins that are placed at fixed points in a neighborhood or locality. MSW is picked up by the municipality, or its designate, according to a set schedule.

customers. Municipalities often license private operators and may designate collection areas to encourage collection efficiencies. Collected MSW can be separated or mixed, depending on local regulations. Generators can be required to separate their waste at source, e.g., into “wet” (food waste, organic matter) and “dry” (recyclables), and possibly a third stream of “waste,” or residue. Waste that is un-segregated could be separated into organic and recycling streams at a sorting facility. The degree of separation can vary over time and by city. ‘Separation’ can be False Creek, a misnomer as waste is not actually separated Vancouver, Canada

3. Curbside Pick-Up: Users leave their garbage directly outside their homes according to a garbage pick-up schedule set with the local authorities (secondary house-tohouse collectors not typical). 4. Self Delivered: Generators deliver the waste directly to disposal sites or transfer stations, or hire third-party operators (or the municipality). 5. Contracted or Delegated Service: Businesses hire firms (or municipality with municipal facilities) who arrange collection schedules and charges with

©

iStockphoto.com/brytta

14


Photo: Cyrus Tata

Separate garbage containers, Singapore

but rather is placed out for collection in separate containers without first being ‘mixed’ together. Often, especially in developing countries, MSW is not separated or sorted before it is taken for disposal, but recyclables are removed by waste pickers prior to collection, during the collection process, and at disposal sites. The degree of source separation impacts the total amount of material recycled and the quality of secondary materials that can be supplied. Recyclables recovered from mixed waste, for example, tend to be contaminated, reducing marketing possibilities. However, source separation and separate collection can add costs to the waste collection process. Collection programs need to be differentiated by type of generator. Often more attention is devoted to residential waste even though this is usually less than 50% of the total waste stream. Waste generated by the ICI sector tends to be collected better, because of more efficient containerization and purpose-built vehicles, and benefits from the collection of fees. Residential waste collection, on the other hand, tends to be more expensive to collect per tonne as

waste is more dispersed. Annex G provides data for MSW collection in cities over 100,000. The percent of MSW collected varies by national income and by region. Higher income countries tend to have higher collection efficiency although less of the solid waste management budget goes towards collection. In low-income countries, collection services make up the bulk of a municipality’s SWM budget (as high as 80 to 90% in many cases), yet collection rates tend to be much lower, leading to lower collection frequency and efficiency. In highincome countries, although collection costs can represent less than 10% of a municipality’s budget, collection rates are usually higher than 90% on average and collection methods tend to be mechanized, efficient, and frequent. While total collection budgets are higher, they are proportionally lower as other budget items increase. For further information on estimated solid waste management costs according to income level, please refer to Annex E. The degree and sophistication of waste picking influences overall collection. In cities like Buenos Aires, waste pickers tend to remove recyclables


after the waste is placed curbside. The resulting scattered waste is more costly to collect: in some cases the value of recyclables are less than the extra costs associated with collecting the disturbed waste. In some cities informal waste pickers have strong links to the waste program and municipally sanctioned crews can be prevented from accessing the waste as informal waste pickers process the waste. Waste pickers can be formally or informally organized into groups or unions with varying degrees of autonomy and political voice. Containerization is an important aspect for waste collection, particularly from residential generators. If waste is not set out for collection in closed containers it can be disturbed by vermin such as dogs and rats, and it can become water-logged, or set afire.

MSW Collection by Region Figure 5 shows MSW collection efficiency by region. Regions with low-income countries tend to have low collection rates. South Asia and Africa are the lowest with 65% and 46% respectively. Not surprisingly, OECD countries tend to have the highest collection efficiency at 98%. Figure 4. Waste Collection by Income

FIG. 4

Waste Collection Rates by Income 100% 90% 80% 70% 60% 50%

Frequency of collection is an important aspect readily under a municipality’s control. From a health perspective, no more than weekly collection is needed. However in some cities, largely because of culture and habituation, three-times per day residential collection is offered (e.g. Shanghai). Good waste collection programming requires an ongoing iterative approach between collection crews and generators (usually households). Therefore, waste generators should be aware of the true costs of collection, and ideally be charged for these directly.

40% 30% 20% 10% 0%

High Income

Upper Middle Income

Lower Middle Income

Lower Income

Figure 5. Waste Collection by Region

FIG. 5

Waste Collection Rates by Region 100% 90%

MSW Collection by Income

80%

The data show that the average waste collection rates are directly related to income levels. Low-income countries have low collection rates, around 41%, while high-income countries have higher collection rates averaging 98%. Figure 4 shows the average collection percentage by income. Annex K details MSW collection rates by country.

60%

70% 50% 40% 30% 20% 10% 0%

OECD

MENA

LAC

ECA

EAP

SAR

AFR

15

16


Waste Composition At a Glance: ` Waste composition is influenced by factors such as culture, economic development, climate, and energy sources; composition impacts how often waste is collected and how it is disposed. ` Low-income countries have the highest proportion of organic waste. ` Paper, plastics, and other inorganic materials make up the highest proportion of MSW in highincome countries. ` By region, EAP has the highest proportion of organic waste at 62%, while OECD countries have the least at 27%, although total amount of organic waste is still highest in OECD countries. ` Although waste composition is usually provided by weight, as a country’s affluence increases, waste volumes tend to be more important, especially with regard to collection: organics and inerts generally decrease in relative terms, while increasing paper and plastic increases overall waste volumes.

In the municipal solid waste stream, waste is broadly classified into organic and inorganic. In this study, waste composition is categorized as organic, paper, plastic, glass, metals, and ‘other.’ These categories can be further refined, however, these six categories are usually sufficient for general solid waste planning purposes. Table 9 describes the different types of waste and their sources. An important component that needs to be considered is ‘construction and demolition waste’ (C&D), such as building rubble, concrete and masonry. In some cities this can represent as much TABLE 9

Types of Waste and Their Sources

Type

as 40% of the total waste stream. However, in this report, C&D waste is not included unless specifically identified. A separate case-by-case review is recommended for specific cities. Industrial, Commercial and Institutional (ICI) waste also needs further local refinement. Many industrial processes have specific wastes and by-products. In most cities this material, with its relatively easier flow and quality control, is the first material to be recycled. Some industrial process waste requires specific treatment. For most MSW management plans industrial by-products are not

Sources

Organic

Food scraps, yard (leaves, grass, brush) waste, wood, process residues

Paper

Paper scraps, cardboard, newspapers, magazines, bags, boxes, wrapping paper, telephone books, shredded paper, paper beverage cups. Strictly speaking paper is organic but unless it is contaminated by food residue, paper is not classified as organic.

Plastic

Bottles, packaging, containers, bags, lids, cups

Glass

Bottles, broken glassware, light bulbs, colored glass

Metal

Cans, foil, tins, non-hazardous aerosol cans, appliances (white goods), railings, bicycles

Other

Textiles, leather, rubber, multi-laminates, e-waste, appliances, ash, other inert materials

Figure 6. Waste Composition in China


Metal 1%

2000: Population Using Coal

Others 10%

2000: Population Using Gas

FIG. 6

Glass 2%

Waste Composition in China

Plastic 13%

Organic 65%

Organic 41%

Others 47%

Paper 9%

Metal 1%

Source: Hoornweg 2005

Glass 2%

Plastic 4%

Paper 5%

Municipal Waste Genereated from Population Using Coal for household heating = 49,500,000 tons Municipal Waste Genereated from Population Using Gas for household heating = 100,500,000 tons Total Municipal Waste Generation in 2000 = 150,000,000 tons

included in waste composition analyses, however household and general waste should be included since it is usually disposed at common facilities, and in most cities waste from the ICI sector represents the largest fraction of the waste collected.

fication of MSW composition based on region (See Annex N). In high-income countries, an integrated approach for organic waste is particularly important, Figure 7. Global Solid Waste Composition as organic waste may be diverted to water-borne sewers, which is usually a more expensive option.

Waste composition is influenced by many factors, such as level of economic development, cultural norms, geographical location, energy sources, and climate. As a country urbanizes and populations become wealthier, consumption of inorganic materials (such as plastics, paper, and aluminum) increases, while the relative organic fraction decreases. Generally, lowand middle-income countries have a high percentage of organic matter in the urban waste stream, ranging from 40 to 85% of the total. Paper, plastic, glass, and metal fractions increase in the waste stream of middle- and high-income countries. For data on MSW composition in cities with a population of over 100,000, please refer to Annex I.

Geography influences waste composition by determining building materials (e.g. wood versus steel), ash content (often from household heating), amount of street sweepings (can be as much as 10% of a city’s waste stream in dry locations), and horticultural waste. The type of energy source

Figure 8 illustrates the differences between low- and high-income countries: organics make up 64% of the MSW stream for low-income countries and paper only 5%, whereas in high-income countries it is 28% and 31% respectively. The IPCC uses its own classi-

Other 18%

FIG. 7

Global Solid Waste Composition

Metal 4% Organic 46%

Glass 5% Plastic 10%

Paper 17%

17

18


in a location can have an impact on the composition of MSW generated. This is especially true in low-income countries or regions where energy for cooking, heating, and lighting might not come from district heating systems or the electricity grid. For example, Figure 6 shows the difference in waste composition in China between a section of the population that uses coal and another that uses natural gas for space heating. The ‘other’ category is clearly higher: 47% when coal is used, and an ash residue is included, as opposed to 10% when natural gas is used for home heating. Climate can also influence waste generation in a city, country, or region. For example, in Ulan Bator, Mongolia, ash makes up 60% of the MSW generated in the winter, but only 20% in the summer (UNEP/GRID-Arendal 2004). Precipitation is also important in waste composition, particularly when measured by mass, as un-containerized waste can absorb significant amounts of water from rain and snow. Humidity also influences waste composition by influencing moisture content.

Methodology This report includes waste composition data that was available for 105 countries from various sources. Please see Annex M for further information on MSW composition data by country. Waste composition data is generally available as percentages of the various waste streams, commonly divided into the categories shown in Table 10. In some cases, ‘other’ wastes are further disaggregated into textiles, rubber, ash, etc. However, for the purposes of standardization and simplification the ‘other’ category in this report includes all of these wastes. Although the definitions and methodologies for determining composition are not always provided or standardized in the waste studies referenced, the compositions for MSW are assumed to be based on wet weight. Each waste category was calculated using waste generation figures from individual

countries. The total waste composition figures by income and by region were then aggregated. Figure 7 shows the MSW composition for the entire world in 2009. Organic waste comprises the majority of MSW, followed by paper, metal, other wastes, plastic, and glass. These are only approximate values, given that the data sets are from various years.

Waste Composition by Income As Figures 8 a-d show, the organic fraction tends to be highest in low-income countries and lowest in high-income countries. Total amount of organic waste tends to increase steadily as affluence increases at a slower rate than the non-organic fraction. Low-income countries have an organic fraction of 64% compared to 28% in high-income countries. The data presented in Figure 9 illustrates solid waste composition by income as compared between current values and values projected for 2025. Annex J provides data for MSW projections for 2025 by income level. Table 10 represents a compilation of composition values of current day data presented in Annex M, and specific reports for larger countries such as China and India. Estimates for waste composition in 2025 are based on trends observed in OECD countries and authors’ projections.

Waste Composition by Region MSW composition by region is shown in Figures 10 a-g. The East Asia and the Pacific Region has the highest fraction of organic waste (62%) compared to OECD countries, which have the least (27%). The amount of paper, glass, and metals found in the MSW stream are the highest in OECD countries (32%, 7%, and 6%, respectively) and lowest in the South Asia Region (4% for paper and 1% for both glass and metals). Annex J provides data for MSW projections for 2025 by region.


Figure 8. Waste Composition by Income

a. Waste Composition in Low-Income Countries

19

b. Waste Composition in Lower Middle-Income Countries

FIG. 8

Other 15%

Other 17%

Waste Composition by Income

Metal 2%

Metal 3%

Glass 3%

Glass 3%

Organic 64%

Plastic 8%

Organic 59%

Plastic 12%

Paper 5%

Paper 9%

c. Waste Composition in Upper Middle-Income Countries

d. Waste Composition in High-Income Countries

Other 13%

Other 17% Organic 28%

Metal 3% Glass 5%

Metal 6%

Organic 54%

Plastic 11%

Glass 7%

Plastic 11%

Paper 14%

Paper 31%

TABLE 10

CURRENT ESTIMATES* Source:

Income Level

Organic (%)

Paper (%)

Plastic (%)

Glass (%)

Metal (%)

Other (%)

Low Income

64

5

8

3

3

17

Lower Middle Income

59

9

12

3

2

15

Upper Middle Income

54

14

11

5

3

13

High Income

28

31

11

7

6

17

Glass (%)

Metal (%)

Other (%)

2025 ESTIMATES** Income Level

Organic (%)

Paper (%)

Plastic (%)

Low Income

62

6

9

3

3

17

Lower Middle Income

55

10

13

4

3

15

Upper Middle Income

50

15

12

4

4

15

High Income

28

30

11

7

6

18

*Source year: varies, see Annex C on Data Availability. **Source: By author from global trends, and Annex J.

Types of Waste Composition by Income Level

20


Figure 9. Solid Waste Composition

CURRENT

FIG. 9

Solid Waste Composition by Income and Year

2025

Other 17%

Metal

Glass 3% 3% Plastic 8% Paper 5%

Other Metal 17% 3% Glass 3% Plastic 9% Paper 6%

Low Income Organic 64%

75 MT*

Organic 62%

201 MT Other 15%

Other 15%

Metal 2% Glass 3%

Lower Middle Income Organic 59%

Plastic 12%

Metal 3% Glass 4% Organic 55%

Plastic 13%

Paper 9%

Paper 10%

369 MT

Metal 3%

956 MT Other 15%

Other 13%

Upper Middle Income

Glass 5% Organic 54%

Plastic 11%

Metal 4% Glass 4%

Organic 50%

Plastic 12%

Paper 14%

Paper 15%

243 MT High Income

Others 17%

426 MT

Other 18%

Organic 28%

Metal 6%

Organic 28% Metal 6%

Glass 7%

Glass 7%

Plastic 11% Paper 31%

602 MT Source: Current data vary by country. *Total annual waste volume in millions of tonnes

Plastic 11%

Paper 30%

686 MT


21

Figure 10. Global Solid Waste Composition a. AFR Waste Composition Other 13% Metal 4% Glass 4% Organic 57%

Waste Composition by Region

Other 10%

Metal 2% Glass 3%

Plastic 13%

FIG. 10

b. EAP Waste Composition

Plastic 13%

Organic 62%

Paper 10%

Paper 9%

c. ECA Waste Composition

d. SAR Composition

Other 19% Other 37%

Metal 5%

Organic 47

Glass 7% Plastic 8%

Organic 50% Metal 1% Glass Paper 1% Plastic 4% 7%

Paper 14%

g. LAC Waste Composition

e. MENA Waste Composition Other Metal 10% 3% Glass 3%

Metal 2% Glass 4%

Plastic 9%

Organic 54%

Plastic 12% Organic 61%

Paper 14%

Other 12%

Paper 16%

f. OECD Waste Composition Other 17% Organic 27%

Metal 6% Glass 7% Plastic 11% Paper 32%


Waste Disposal At a Glance: ` Landfilling and thermal treatment of waste are the most common methods of MSW disposal in high-income countries. ` Although quantitative data is not readily available, most low- and lower middle-income countries dispose of their waste in open dumps. ` Several middle-income countries have poorly operated landfills; disposal should likely be classified as controlled dumping.

Waste disposal data are the most difficult to collect. Many countries do not collect waste disposal data at the national level, making comparisons across income levels and regions difficult. Furthermore, in cases where data is available, the methodology of how disposal is calculated and the definitions used for each of the categories is often either not known or not consistent. For example, some countries only give the percentage of waste that is dumped or sent to a landfill, the rest falls under ‘other’ disposal. In other cases, compostable and recyclable material is removed before the waste reaches the disposal site and is not included in waste disposal statistics. Please refer to Annex H for MSW disposal data for cities with populations Figure 11. Total MSW Disposed Worldwide over 100,000.

Methodology Waste disposal data was available for 87 countries through various sources. Annex L presents MSW disposal methods data by country. Waste disposal data sets are generally available as percentages of the various waste disposal options, commonly divided into the categories shown in Table 10. Although the definitions and methodologies for calculating waste disposal methods and quantities are not always provided or standardized in waste studies, the disposal of MSW is assumed to be based on wet weight. Each waste disposal category was calculated using waste generation figures for the individual country. The total waste disposal figures by income and by region were then aggregated. Figure 11 shows current annual global MSW disposal for the entire world. These are only approximate values, given that the data is from various years.

FIG. 11

Total MSW Disposed of Worldwide 400

Amount Disposed (millions tons/year)

22

350 300

MSW Disposal by Income

250

Table 11 shows in further detail how MSW disposal varies according to country income level.

200 150 100 50 0

Landfill

Recycled

WTE

Dump

Disposal Options

Compost

Other

Figures 12 and 13 illustrate the differences in MSW disposal methods according to country income level, in particular low-income and upper middle-income countries.



High Income

TABLE 11

Upper Middle Income

Dumps

0.05

Dumps

44

Landfills

250

Landfills

80

Compost

66

Compost

1.3

Recycled

129

Recycled

Incineration

122

Incineration

0.18

Other

8.4

Other

21

1.9

Low Income Dumps

Lower Mid dle Income 0.47

Dumps

23

27*

Landfills

2.2

Landfills

6.1

Compost

0.05

Compost

1.2

Recycled

0.02

Recycled

2.9

Incineration

0.05

Incineration

0.12

Other

0.97

Other

18 *This value is relatively high due to the inclusion of China.

MSW Disposal by Income (million tonnes)

24


Table 12 contrasts the world’s richest (OECD) and poorest (Africa) regions. Populations in the two regions are roughly equal, yet the OECD region produces about 100 times the waste of Africa (these disparities are parallel to regional differ-

FIG. 12

Figure 12. Low-Income Countries Waste Disposal Low-Income Countries Waste Disposal

ences in GHG emissions). Africa’s collected waste is almost exclusively dumped or sent to landfills, while more than 60% of OECD’s waste is diverted from landfill.

FIG. 13

Figure 13. Upper Middle-Income Countries Waste Disposal Upper Middle-Income Countries Waste Disposal

Compost 1%

Dumps 13%

Other 26%

Recycled 1%

Income 0% Other 6% Dumps 33%

Income 1% Recycled 0% Compost 1%

Landfills 59%

Landfills 59%

TABLE 12

MSW Disposal Dumps Source: Hoornweg 2005 in two contrasting regions (million Landfills tonnes) Compost

AFR

OECD 2.3

Source: Hoornweg 2005

Dumps

—

2.6

Landfills

242

0.05

Compost

66

Recycled

0.14

Recycled

125

Incineration

0.05

Incineration

120

Other

0.11

Other

20


25

Waste and the Environment Integrated Solid Waste Management Integrated solid waste management (ISWM) reflects the need to approach solid waste in a comprehensive manner with careful selection and sustained application of appropriate technology, working conditions, and establishment of a ‘social license’ between the community and designated waste management authorities (most commonly local government). ISWM is based on both a high degree of professionalism on behalf of solid

waste managers; and on the appreciation of the critical role that the community, employees, and local (and increasingly global) ecosystems have in effective SWM. ISWM should be driven by clear objectives and is based on the hierarchy of waste management: reduce, reuse, recycle — often adding a fourth ‘R’ for recovery. These waste diversion options are then followed by incineration and landfill, or other disposal options. Please refer to Box 3 for a detailed list describing the components of an ISWM Plan.

Components of an Integrated Solid Waste Management Plan An integrated Solid Waste Management plan should include the following sections:

conjunction with facilities and practices and ways in which this information will be regularly reported;

`Àll municipal policies, aims, objectives, and initia-

`Àssociated institutional reforms and regulatory

tives related to waste management;

arrangements needed to support the plan;

``The character and scale of the city, natural condi-

``Financial assessment of the plan, including anal-

tions, climate, development and distribution of population;

ysis of both investment and recurrent costs associated with the proposed facilities and services, over the lifetime of the plan (or facilities);

``Data on all waste generation, including data covering both recent years and projections over the lifetime of the plan (usually 15-25 years). This should include data on MSW composition and other characteristics, such as moisture content and density (dry weight), present and predicted;

options) for waste collection, transportation, treatment, and disposal of the defined types and quantities of solid wastes (this must address options for all types of solid waste arising);

with developing and operating the plan including estimated subsidy transfers and user fees;

``The requirements for managing all non-MSW arisings, what facilities are required, who will provide them and the related services, and how such facilities and services will be paid for;

BOX 3

`Ìdentify all proposed options (and combination of

`Àll the sources of finance and revenues associated

``The proposed implementation plan covering a

`Èvaluation of the Best Practical Environmental

period of at least 5-10 years, with an immediate action plan detailing actions set out for the first 2-3 years;

Option(s), integrating balanced assessments of all technical, environmental, social, and financial issues;

`Òutline of public consultations carried out during preparation of the plan and proposed in future;

``The proposed plan, specifying the amount, scale, and distribution of collection, transportation, treatment and disposal systems to be developed, with proposed waste mass flows proposed through each;

`Òutline of the detailed program to be used to site

``Specifications on the proposed on-going moni-

`Àn assessment of GHG emissions and the role of

toring and controls that will be implemented in

key waste management facilities, e.g. landfills, compost plants, and transfer stations.

MSW in the city’s overall urban metabolism.

26

BOX 4

Integrated Sustainable Waste Management Framework have an interest or roles. All stakeholders should be identified and where practical involved in creating a SWM program.

Elements (Process): include the technical aspects

Sus tai na b

Stakeholders: include individuals or groups that y ilit

of solid waste management. All stakeholders impact one or more of the elements. The elements need to be considered simultaneously when creating an SWM program in order to have an efficient and effective system.

Aspects (Policies and Impacts): encompass the regulatory, environmental and financial realities in which the waste management system operates. Specific aspects can be changeable, e.g. a community increases influence or environmental regulations are tightened. Measures and priorities are created based on these various local, national and global aspects.

As outlined by the Dutch NGO, WASTE, ISWM is based on four principles: equity for all citizens to have access to waste management systems for public health reasons; effectiveness of the waste management system to safely remove the waste; efficiency to maximize benefits, minimize costs, and optimize the use of resources; and sustainability of the system from a technical, environmental, social (cultural), economic, financial, institutional, and political perspective (van de Klundert and Anschütz 2001). There are three interdependent and interconnected dimensions of ISWM, which need to be addressed simultaneously when designing a solid waste management system: stakeholders, elements, and aspects. Please refer to Box 4 for further details on the interconnected dimensions of ISWM. An alternative framework is provided by UN-HABITAT, which identifies three key system elements in ISWM: public health, environmental protection, and resource management (UN-Habitat 2009).

Local/Regulatory Authorities NGOs/CBOs Service Users Informal/Formal Sector Donor Agencies

Stakeholders Elements

Aspects

Generation and Separation Collection Transfer Treatment and Disposal Recovery 3 R’s

Environmental Political/Legal Institutional Socio-Cultural Financial/Economic Technical and Performance

Adapted van de Klundert and Anschütz 2001. Adapted fromfrom van de Klundert and Anschütz 2001.

Public Health: In most jurisdictions, public health concerns have been the basis for solid waste management programs, as solid waste management is essential to maintaining public health. Solid waste that is not properly collected and disposed can be a breeding ground for insects, vermin, and scavenging animals, and can thus pass on air- and water-borne diseases. Surveys conducted by UN-Habitat show that in areas where waste is not collected frequently, the incidence of diarrhea is twice as high and acute respiratory infections six times higher than in areas where collection is frequent (UN-Habitat 2009). Environmental Protection: Poorly collected or improperly disposed of waste can have a detrimental impact on the environment. In low- and middle-income countries, MSW is often dumped in low-lying areas and land adjacent to slums. Lack of enforced regulations enables potentially infectious medical and hazardous waste to be mixed with MSW, which is harmful to waste pickers and the environment. Environmental threats include contamination of groundwater and surface water


by leachate, as well as air pollution from burning of waste that is not properly collected and disposed. Resource Management: MSW can represent a considerable potential resource. In recent years, the global market for recyclables has increased significantly. The world market for post consumer scrap metal is estimated at 400 million tonnes annually and around 175 million tonnes annually for paper and cardboard (UN-Habitat 2009). This represents a global value of at least $30 billion per year. Recycling, particularly in low- and middleincome countries, occurs through an active, although usually informal, sector. Producing new products with secondary materials can save significant energy. For example, producing aluminum from recycled aluminum requires 95% less energy than producing it from virgin materials. As the

Most preferred option

27

cost of virgin materials and their environmental impact increases, the relative value of secondary materials is expected to increase.

Waste Disposal Options The waste management sector follows a generally accepted hierarchy. The earliest known usage of the ‘waste management hierarchy’ appears to be Ontario’s Pollution Probe in the early 1970s. The hierarchy started as the ‘three Rs’ — reduce, reuse, recycle — but now a fourth R is frequently added — recovery. The hierarchy responds to financial, environmental, social and management considerations. The hierarchy also encourages minimization of GHG emissions. See Figure 14 for the waste hierarchy.

FIG. 14

Waste Hierarchy

Reduce Reuse Waste Diversion

Recycle Recover

(digestion, composting)

Landfill Incineration

(with energy recovery)

Waste Disposal

Controlled Dump* Least preferred option *As a minimum, waste should be disposed at a “controlled dump,” which includes site selection, controlled access, and where practical, compaction of waste. Incineration requires a complimentary sanitary landfill, as bottom ash, non-combustibles and by-passed waste needs to be landfilled.

28


Photo: Eric Miller/World Bank

Maputo – Fapel, paper mill and paper recycling factory

1. Waste Reduction: Waste or source reduction initiatives (including prevention, minimization, and reuse) seek to reduce the quantity of waste at generation points by redesigning products or changing patterns of production and consumption. A reduction in waste generation has a two-fold benefit in terms of greenhouse gas emission reductions. First, the emissions associated with material and product manufacture are avoided. The second benefit is eliminating the emissions associated with the avoided waste management activities. 2. Recycling and Materials Recovery: The key advantages of recycling and recovery are reduced quantities of disposed waste and the return of materials to the economy. In many developing countries, informal waste pickers at collection points and disposal sites recover a significant portion of discards. In China, for example, about 20% of discards are recovered for recycling, largely attributable to informal waste picking (Hoornweg et al 2005). Related

GHG emissions come from the carbon dioxide associated with electricity consumption for the operation of material recovery facilities. Informal recycling by waste pickers will have little GHG emissions, except for processing the materials for sale or reuse, which can be relatively high if improperly burned, e.g. metal recovery from e-waste. 3. Aerobic Composting and Anaerobic Digestion: Composting with windrows or enclosed vessels is intended to be an aerobic (with oxygen) operation that avoids the formation of methane associated with anaerobic conditions (without oxygen). When using an anaerobic digestion process, organic waste is treated in an enclosed vessel. Often associated with wastewater treatment facilities, anaerobic digestion will generate methane that can either be flared or used to generate heat and/or electricity. Generally speaking, composting is less complex, more forgiving, and less costly than anaerobic


digestion. Methane is an intended by-product of anaerobic digestion and can be collected and combusted. Experience from many jurisdictions shows that composting source separated organics significantly reduces contamination of the finished compost, rather than processing mixed MSW with front-end or back-end separation. 4. Incineration: Incineration of waste (with energy recovery) can reduce the volume of disposed waste by up to 90%. These high volume reductions are seen only in waste streams with very high amounts of packaging materials, paper, cardboard, plastics and horticultural waste. Recovering the energy value embedded in waste prior to final disposal is considered preferable to direct landfilling — assuming pollution control requirements and costs are adequately addressed. Typically, incineration without energy recovery (or non-autogenic combustion, the need to regularly add fuel) is not a preferred option due to costs and pollution. Open-burning of waste is particularly discouraged due to severe air pollution associated with low temperature combustion.

Operation and Engineering Measures

29

5. Landfill: The waste or residue from other processes should be sent to a disposal site. Landfills are a common final disposal site for waste and should be engineered and operated to protect the environment and public health. Landfill gas (LFG), produced from the anaerobic decomposition of organic matter, can be recovered and the methane (about 50% of LFG) burned with or without energy recovery to reduce GHG emissions. Proper landfilling is often lacking, especially in developing countries. Landfilling usually progresses from open-dumping, controlled dumping, controlled landfilling, to sanitary landfilling (see Table 13).

Waste and Climate Change GHG emissions from MSW have emerged as a major concern as post-consumer waste is estimated to account for almost 5% (1,460 mtCO2e) of total global greenhouse gas emissions. Solid waste also includes significant embodied GHG emissions. For example, most of the GHG emissions associated with paper occur before it becomes MSW. Encouraging waste minimization through MSW programs can therefore have significant up-stream GHG minimization benefits.

Leachate Management

Landfill Gas Management

Semi-controlled Dumps

Few controls; some directed placement of waste; informal waste picking; no engineering measures

Unrestricted contaminant release

None

Controlled Dump

Registration and placement/compaction of waste; surface water monitoring; no engineering measures

Unrestricted contaminant release

None

Engineered Landfill/ Controlled Landfill

Registration and placement/compaction of waste; uses daily cover material; surface and ground water monitoring; infrastructure and liner in place

Containment and some level of Passive ventilation or flaring leachate treatment; reduced leachate volume through waste cover

Sanitary Landfill

Registration and placement/compaction of waste; uses daily cover; measures for final top cover and closure; proper siting, infrastructure; liner and leachate treatment in place and post-closure plan.

Containment and leachate treatment (often biological and physico-chemical treatment)

Flaring with or without energy recovery

TABLE 13

Landfill Classifications

30


TABLE 14

Landfill Methane Emissions and Total GHG Emissions for Selected Countries

Country

Methane Emissions from Post-Consumer Municipal Waste Disposa* (MtCO2e)

Greenhouse Gas Emissions** (CO2, CH4, N2O) (MtCO2e)

% Methane from Disposal Sites Relative to Total GHG Emissions

Brazil

16

659

2.4%

China

45

3,650

1.2%

India

14

1,210

1.1%

Mexico

31

383

8.1%

South Africa

16

380

4.3%

*EPA 2006a. **UNFCCC 2005.

Methane from landfills represents 12% of total global methane emissions (EPA 2006b). Landfills are responsible for almost half of the methane emissions attributed to the municipal waste sector in 2010 (IPCC 2007).4 The level of methane from landfills varies by country, depending on waste composition, climatic conditions (ambient temperature, precipitation) and waste disposal practices. Table 14 highlights some examples. Organic biomass5 decomposes anaerobically in a sanitary landfill. Landfill gas, a by-product of the anaerobic decomposition is composed of methane (typically about 50%) with the balance being carbon dioxide and other gases. Methane, which 4

Wastewater management adds an equal amount of methane to the atmosphere. Organic biomass excludes organic waste such as plastics that are derived from fossil energy sources.

5

has a Global Warming Potential 21 times greater than carbon dioxide, is the second most common greenhouse gas after carbon dioxide. Greenhouse gas emissions from waste management can readily be reduced. Within the European Union, the rate of GHG emissions from waste has declined from 69 mtCO2e per year to 32 million tCO2e per year from 1990 to 2007 (ISWA 2009).

Greenhouse Gas Mitigation Opportunities Efforts to reduce emissions from the municipal solid waste sector include generating less waste, improving the efficiency of waste collection, expanding recycling, methane avoidance (aerobic composting, anaerobic digestion with combustion

A transfer station in Amman, Jordan

Photo: Perinaz Bhada-Tata


Waste Management Component

Technology Options

Waste Reduction

Design of longer-lasting and reusable products; reduced consumption.

Waste Collection

Use of alternative, non-fossil fuels (bio-fuel, natural gas).

Recycling/Materials Recovery

Materials recovery facility (MRF) to process source separated materials or mixed waste, although source separated is the preferred option as the materials would have less contamination from other discards. MRFs use a combination of manual and mechanical sorting options. Waste pickers could be used as a source of labor for manual sorting stages.

Composting/Anaerobic Digestion

Institute composting programs ideally with source separated organics. As with recyclables source separated materials reduce the contamination associated with recovery from mixed waste. Compost the organic material after digestion to produce a useful soil conditioner and avoid landfill disposal. Finished compost applied to soils is also an important method to reduce GHG emissions by reducing nitrogen requirements and associated GHG emissions.

Incineration/Waste-to-energy/ Refuse–Derived Fuel (RDF)

Use the combustible fraction of waste as a fuel either in a dedicated combustion facility (incineration) with or without energy recovery or as RDF in a solid fuel boiler.

Landfill

Capture the methane generated in disposal sites and flare or use as a renewable energy resource.

of produced methane and capture, treatment and use of landfill gas). Energy generated from methane combustion can displace other fossil fuels either as a process energy resource or as electricity. Suitable technology options by waste management component are provided in Table 15.

Policy Recommendations for Reducing GHG Emissions Governments have a range of policy options to encourage waste management practices that will reduce greenhouse gas emissions. Practical approaches that could be applied in most cities include: ` Public education to inform people about their options to reduce waste generation and increase recycling and composting. ` Pricing mechanisms, such as product charges can stimulate consumer behavior to reduce waste generation and increase recycling. A product charge is a cost assessment added to the price of a product and is tied to the cost of the desired waste management system. Consumers would pay for the waste management service

when they buy the product. The fees collected would be directed to municipalities relative to the waste generated. An example of this economic mechanism is an excise tax on tires assessed by most states in the US. Product charges are a policy mechanism often better implemented by regional or national governments. ` Another pricing mechanism well suited to urban areas is user charges tied to quantity of waste disposed. Consumers who separate recyclables pay a lower fee for waste disposal. This pricing policy can work well in locations where waste collection is from individual households so that waste quantities for disposal can be readily monitored. However, it may not be practical in many areas in developing countries, particularly in those where there are communal collection points associated with multi-unit households (such as apartment user charges tied to quantity or volume). ` Preferential procurement policies and pricing to stimulate demand for products made with recycled post-consumer waste. Use of compost in public parks and other property owned by cities.

31

TABLE 15

Technical GHG Mitigation Opportunities by Waste Management Component

32


A Note on the Reliability of Solid Waste Data Solid waste data should be considered with a degree of caution due to global inconsistencies in definitions, data collection methodologies, and completeness. The reliability of the data is influenced by: `` Undefined words or phrases `` Inconsistent or omitted units `` Dates, methodologies, or sources of data not indicated `` Estimates made without basis `` Incomplete or inconsistent data (please see Annexes C and D for further information on available data) `` Information collected at a non-representative moment In most low- and middle-income countries, the reliability of solid waste data is further compromised by large seasonal variations (e.g. seasonal rains and un-containerized waste, horticultural variations), incomplete waste collection and disposal (e.g. a significant level of waste is disposed directly through local burning or thrown in waterways and low lying areas), and a lack of weight scales at landfill sites to record waste quantities.

middle-income countries where the informal sector removes a large fraction of recyclables. Additionally, in most low- and middle-income countries, waste collection rates are low and formal service does not extend to all communities, thereby reducing the quantities of waste delivered to final disposal sites. Measuring waste quantities for final disposal is practical for municipal purposes. Large variation in waste quantity and composition can be observed if the economic situation changes, yet growing waste quantities associated with increasing GNP are not necessarily a true reflection of increased waste; they might be changes in the relative recoverable value of the secondary materials and improvements in overall collection efficiency. Waste composition specifies the components of the waste stream as a percentage of the total mass or volume. The component categories used within this report are: `` organics (i.e. compostables such as food, yard, and wood wastes) `` paper `` plastic `` glass `` metal

Rarely is it disclosed at what stage the waste generation rates and composition were determined, and whether they were estimated or physically measured. The most accurate method measures the waste generated at source before any recycling, composting, burning, or open dumping takes place. However, the generation rate and composition are commonly calculated using waste quantities arriving at the final disposal site. This method of measurement does not fully represent the waste stream because waste can be diverted prior to final disposal, especially in low- and

`` others (includes ceramics, textiles, leather, rubber, bones, inerts, ashes, coconut husks, bulky wastes, household goods) ‘Others’ wastes should be differentiated into two categories: other-residue and other-consumer products. Other-residue is made up of ash, inerts, dirt, and sweepings and is a significant component of the waste stream in low- and middle-income countries. Other-consumer products consist of


bulky wastes, household appliances, electronics, and multi-material packaging (e.g., tetrapaks and blister packaging). This waste stream is much more significant in high-income countries and differs from other-residue in that the volumes are much higher per kilogram of waste and are generally combustible. It is important to cite whether the percentages are given on a dry or wet basis, because component percentages will differ markedly depending on moisture content. Rarely is it indicated within a waste study whether the percentage is on a wet or dry basis, or based on volume or mass. It is assumed that the composition was determined on a wet basis. Probably both mass and volume measurements were used depending upon the country. Low- and middle-income countries would be more inclined to use volume since it does not require sophisticated measuring equipment and can be estimated. High-income countries usually use mass as a basis since they have greater funding resources and support to complete a more accurate waste characterization.

Another major inconsistency among the various waste studies is the use of imperial units versus metric units. Frequently the imperial ton and the metric tonne are interchanged when reporting waste quantities. Data are also denoted by the letter “t” to denote the unit, causing the true value to be unknown. Within this report, all of the units are metric, unless clearly noted. Waste densities and moisture contents are needed to convert data to a common frame of reference for comparison (e.g. from mass to volume and from wet to dry). Usually the higher the percentage of organic matter, the higher the moisture content and often the higher the density of the waste stream. There are major efforts being done to correct data inconsistencies at the city level. So far, there is no single standard or comprehensive system to measure and monitor city performance and urban quality of life. In response to this need, the Global City Indicators Program (GCIP), based in Toronto, has been developed. The GCIP (please see Annex O) provides a practical means for cities to collect credible information on MSW.

Photo: Cyrus Tata

Bangalore, India

33

A couple salvage old bricks from an area demolished for renovation in Saigon Photo: Tran Thi Hoa

ANNEXES

36


ANNEX A

Map of Regions

Greenland (Den)

Iceland

Faeroe Islands (Den)

The Netherlands Isle of Man (UK)

Canada

Unite Kingdo

Ireland Channel Islands (UK)

Luxembourg Liechtenstein Switzerland Andorra United States

F

Portugal

Spain

Gibraltar (UK)

Bermuda (UK)

Morocco

A Former Spanish Sahara

The Bahamas Cayman Is.(UK)

Mexico

Cuba

Mauritania

1

Haiti

Belize Jamaica Guatemala Honduras El Salvador Nicaragua

Cape Verde

Mali Senegal

The Gambia Guinea-Bissau

Costa Rica Panama

R.B. de Venezuela

Guyana Suriname

Guinea

Sierra Leone Liberia

French Guiana (Fr)

Colombia

Burkina Faso

Côte Ghana d’Ivoire

To Equatorial G São Tomé and Prí

Ecuador Kiribati

Brazil

Peru

Samoa

French Polynesia (Fr) American Samoa (US)

Fiji

Bolivia

Tonga Paraguay Dominican Republic

Puerto Rico (US)

U.S. Virgin Islands (US)

1

St. Martin(Fr) St. Maarten(Neth)

Antigua and Barbuda St. Kitts and Nevis


Dominica St. Lucia Barbados Grenada

R.B. de Venezuela

Chile

Trinidad and Tobago

Poland

Czech Republic Slovak Repu

Uruguay Argentina

Austria

Guadeloupe (Fr)

Martinique (Fr) Aruba Bonaire Curaçao (Neth) (Neth) (Neth)

Germany

Hungary

Slovenia Croatia Bosnia and Italy Herzegovina Serb San Marino Montenegro Koso

2

Vatican City

Mac Albania G

ANNEX

37

IBRD 39177 MARCH 2012

Norway Sweden

Finland

Russian Federation

Estonia Denmark Russian Latvia Fed. Lithuania ed Germany Poland Belarus om Belgium Ukraine Moldova Romania France Italy Bulgaria

2

Monaco

Tunisia

Algeria

a

Turkey

Greece

Georgia Armenia Azerbaijan

Cyprus Lebanon Israel

Syrian Arab Rep.

West Bank and Gaza

Jordan

Malta

Libya

Niger

Benin

Kazakhstan

Arab Rep. of Egypt

Chad

Eritrea

Sudan

Dem.People’s Rep.of Korea

Tajikistan

Pakistan

United Arab Emirates Oman

ogo Guinea íncipe

Cameroon


Bangladesh India

Myanmar

Gabon

Congo

Sri Lanka

Guam (US)

Philippines

Federated States of Micronesia

Brunei Darussalam Malaysia

Marshall Islands

Palau

Maldives

Kenya

Rwanda Dem.Rep.of Burundi Congo Tanzania

N. Mariana Islands (US)

Vietnam Cambodia

Somalia Uganda

Lao P.D.R.

Thailand

Rep. of Yemen

Ethiopia

South Sudan

Japan

Bhutan

Nepal

Djibouti Nigeria

Rep.of Korea

China

Afghanistan

Bahrain Qatar Saudi Arabia

Kyrgyz Rep.

Uzbekistan Turkmenistan

Islamic Rep. of Iran Kuwait

Iraq

Mongolia

Nauru

Singapore Seychelles Comoros

Solomon Islands

Papua New Guinea

Indonesia

Tuvalu

Timor-Leste Angola

Malawi

Zambia Zimbabwe Namibia

Mayotte (Fr)

Mozambique

Botswana

Madagascar

Vanuatu

South Africa

Fiji

Mauritius Réunion (Fr)

Australia

Swaziland

d

Kiribati

New Caledonia (Fr)

Lesotho

Ukraine ublic

The world by region

Romania

bia

Classified according to

ovo Bulgaria FYR cedonia

World Bank analytical grouping

Greece

Low- and middle-income economies East Asia and Pacific Europe and Central Asia Latin America and the Caribbean Middle East and North Africa South Asia

Antarctica

New Zealand

Sub-Saharan Africa

High-income economies OECD Other No data

38


ANNEX B

Map of Income Distribution

Greenland (Den)

Iceland

Faeroe Islands (Den)

The Netherlands Isle of Man (UK)

Canada

Unite Kingdo

Ireland Channel Islands (UK)

Luxembourg Liechtenstein Switzerland Andorra United States

F

Portugal

Spain

Gibraltar (UK)

Bermuda (UK)

Morocco

A Former Spanish Sahara

The Bahamas Cayman Is.(UK)

Mexico

Cuba

Mauritania

1

Haiti

Belize Jamaica Guatemala Honduras El Salvador Nicaragua

Cape Verde

Mali Senegal

The Gambia Guinea-Bissau

Costa Rica Panama

R.B. de Venezuela

Guyana Suriname

Guinea

Sierra Leone Liberia

French Guiana (Fr)

Colombia

Burkina Faso

Côte Ghana d’Ivoire

To Equatorial G São Tomé and Prí

Ecuador Kiribati

Brazil

Peru

Samoa

French Polynesia (Fr) American Samoa (US)

Fiji

Bolivia

Tonga Paraguay Dominican Republic

Puerto Rico (US)

U.S. Virgin Islands (US)

1

St. Martin(Fr) St. Maarten(Neth)

Antigua and Barbuda St. Kitts and Nevis


Dominica St. Lucia Barbados Grenada

R.B. de Venezuela

Chile

Trinidad and Tobago

Polan

Czech Republic Slovak Repu

Uruguay Argentina

Austria

Guadeloupe (Fr)

Martinique (Fr) Aruba Bonaire Curaçao (Neth) (Neth) (Neth)

Germany

Hungary

Slovenia Croatia Bosnia and Italy Herzegovina Ser San Marino Montenegro Koso

2

Vatican City

Mac Albania

ANNEX

39

IBRD 39176 MARCH 2012

Norway Sweden

Finland

Russian Federation

Estonia Denmark Russian Latvia Fed. Lithuania ed Germany Poland Belarus om Belgium Ukraine Moldova Romania France Italy Bulgaria

2

Monaco

Tunisia

Algeria

Kazakhstan

Turkey

Greece

Cyprus Lebanon Israel

Syrian Arab Rep.

West Bank and Gaza

Jordan

Malta

Libya

Niger

Georgia Armenia

Arab Rep. of Egypt

Chad

a Benin

a

Dem.People’s Rep.of Korea

Tajikistan

Pakistan

United Arab Emirates Oman

Togo Guinea íncipe

Cameroon


Bangladesh India

Myanmar

Rep. of Yemen

Gabon

Congo

Sri Lanka

Guam (US)

Philippines

Federated States of Micronesia

Brunei Darussalam Malaysia

Marshall Islands

Palau

Maldives

Kenya

Rwanda Dem.Rep.of Burundi Congo Tanzania

N. Mariana Islands (US)

Vietnam Cambodia

Somalia Uganda

Hong Kong SAR, China

Lao P.D.R.

Thailand

Ethiopia

South Sudan

Japan

Bhutan

Nepal

Djibouti Nigeria

Rep.of Korea

China

Afghanistan

Bahrain Qatar

Eritrea

Sudan

Turkmenistan

Islamic Rep. of Iran Kuwait

Saudi Arabia

Kyrgyz Rep.

Uzbekistan

Azerbaijan

Iraq

Mongolia

Comoros

Solomon Islands

Papua New Guinea

Indonesia

Tuvalu

Timor-Leste Angola

Malawi

Zambia Zimbabwe Namibia

Mayotte (Fr)

Mozambique

Botswana

Vanuatu

Madagascar

Fiji

Mauritius New Caledonia (Fr)

Réunion (Fr)

Australia

Swaziland South Africa

nd

Kiribati

Nauru

Singapore Seychelles

Lesotho

Ukraine ublic New Zealand Romania

rbia

ovo Bulgaria FYR cedonia

As of July 2011, South Sudan is shown independent from Sudan. However, the income data shown on this map is dated 2008, and applies to the former united Sudan.

Greece

The world by income Classified according to World Bank estimates of 2008 GNI per capita

Low ($975 or less) Lower middle ($976–$3,855) Upper middle ($3,856–$11,905) High ($11,906 or more)

Antarctica

No data

40


ANNEX C

Availability of MSW Data by Country Source

Collection

Urban or Total

Year of Data

Source

2006

Denmark Ministry of Foreign Affairs

x

T

2005

UNSD (2009)

x

2002

METAP (2004)

x

U

2002

OECD AFR

x x

2007 2005

UNSD (2009) USAID (2009)

x

T

2007

METAP (2004) UNSD (2009)

HIC

LCR

x

2001

PAHO (2005)

x

T

2007

UNSD (2009)

x

2007

UNSD (2009)

UMI LMI HIC HIC HIC HIC

LCR ECA OECD OECD LCR MENA

x x x x x x

2001 2007 1999 2006 2001 2000

x

T

2007

UNSD (2009)

x

T

2007

UNSD (2009)

x x x

2007 2003 2004

UNSD (2009) OECD (2008) OECD (2008)

Bangladesh4

LI

SAR

x

2004

Barbados

HIC

LCR

x

2001

Belarus

UMI

ECA

x

2005

Belgium Belize Benin2 Bhutan

HIC LMI LI LMI

OECD LCR AFR SAR

x x x x

2006 2001 2005 2007

Bolivia

LMI

LCR

x

2003

Botswana

UMI

AFR

x

1998

Brazil Brunei Darussalam

UMI

LCR

x

2001

HIC

EAP

x

2006

Bulgaria

LMI

ECA

x

2007

Burkina Faso2 Burundi2

LI LI

AFR AFR

x x

2005 2005

PAHO (2005) UNSD (2009) OECD (2008) OECD (2008) PAHO (2005) UNESCWA (2007) Bangladesh Department of Environment (2004) PAHO (2005) Belarus Ministry of Natural Resources (2006) OECD (2008) PAHO (2005) USAID (2009) Phuntsho (2008) Business News Americas (2004) Kgathi and Bolaane (2001) PAHO (2005) Ngoc and Schnitzer (2009) European Environment Agency (2008) USAID (2009) USAID (2009)

Cambodia5

LI

EAP

Cameroon

LMI

AFR

x

2000

Canada Cape Verde2 Central African Republic2 Chad2 Chile

HIC LMI

OECD AFR

x x

1990 2005

Parrot et al. (2009) OECD (2008) USAID (2009)

LI

AFR

x

2005

USAID (2009)

LI UMI

AFR LCR

x x

2005 2001

China

LMI

EAP

x

2004

Colombia Comoros Congo, Dem. Rep.2 Congo, Rep. Costa Rica Cote d'Ivoire2 Croatia6 Cuba

UMI LI

LCR AFR

x x

2001 2003

USAID (2009) PAHO (2005) Hoornweg et al. (2005) PAHO (2005) Payet (2003)

LI

AFR

x

2005

USAID (2009)

LMI UMI LMI HIC UMI

AFR LCR AFR ECA LCR

x x x x x

2005 2001 2005 2008 2001

USAID (2009) PAHO (2005) USAID (2009) Vego (2008) PAHO (2005)

Income Level

Region

Albania1

LMI

ECA

x

Algeria

UMI

MENA

Andorra Angola2 Antigua and Barbuda Argentina Armenia Australia Austria Bahamas, The Bahrain3

HIC LMI

Country

Gen- Year of eration Data

Disposal

x

Year of Data

2002

x

T

2007

UNSD (2009)

x

2005

x x x

T T T

2007 2005 2000

UNSD (2009) UNSD (2009) UNSD (2009)

x x

2003 2005

x

T

2007

Source

METAP (2004)

Belarus Ministry of Natural Resources (2006) OECD (2008) UNSD (2009)

UNSD (2009)

x

T

2002

UNSD (2009)

x

2007

x

U

2000

Kum et al. (2005)

x

2004

x

T

1996

UNSD (2009)

Composition

Year of Data

Source

x

2005

UNSD (2009)

x

2002

METAP (2004)

x

2005

UNSD (2009)

x x x x

2001 2007 2005 2004

UNSD (2009) UNSD (2009) OECD (2008) OECD (2008)

x

2004

UNSD (2009)

x

2004

UNSD (2009)

x x x x

2003 1997 2002 2008

OECD (2008) UNSD (2009) UNSD (2009) Phuntsho (2008)

x

1999

UNSD (2009)

x

2006

x

2006

UNSD (2009) Ngoc and Schnitzer (2009)

x

2000

Ngoc and Schnitzer (2009)

x

2006

UNSD (2009)

x

2004

OECD (2008)

UNSD (2009)

x

2001

x

2004

Kum et al. (2005) Parrot et al. (2009) OECD (2008)

x

2006

UNSD (2009)

x

1998

UNSD (2009)

x x

T T

2001 2003

PAHO (2005) Payet (2003)

x

2005

PAHO (2005)

x

2005

UNSD (2009)

x

T

2001

PAHO (2005)

x

2001

PAHO (2005)

x

2005

UNSD (2009)

x x

T T

2005 2005

UNSD (2009) UNSD (2009)

x x

2006 2005

UNSD (2009) UNSD (2009)

x x

2000 2005

UNSD (2009) UNSD (2009)

ANNEX

41

ANNEX C (continued)

Availability of MSW Data by Country Income Level

Region

Cyprus

HIC

ECA

x

2007

Czech Republic Denmark Dominica Dominican Republic Ecuador3

HIC HIC UMI

OECD OECD LCR

x x x

UMI

LCR

LMI

LCR

Egypt, Arab Rep.

LMI

El Salvador Eritrea2 Estonia

Country


Source

Collection

Urban or Total

Year of Data

Source

Disposal

Year of Data

Source

Composition

Year of Data

Source

x

2007

UNSD (2009)

x

2001

UNSD (2009)

2006 2006 2001

European Environment Agency (2008) OECD (2008) OECD (2008) PAHO (2005)

x x x

T T T

2007 2007 2005

UNSD (2009) UNSD (2009) UNSD (2009)

x x x

2004 2003 2005

OECD (2008) OECD (2008) UNSD (2009)

x x

1996 2005

UNSD (2009) OECD (2008)

x

2001

PAHO (2005)

x

T

2001

PAHO (2005)

x

2001

PAHO (2005)

x

2000

UNSD (2009)

x

2001

PAHO (2005)

x

T

2001

x

2001

PAHO (2005)

MENA

x

2000

METAP (2004)

x

U

2000

x

2000

METAP (2004)

x

2000

METAP (2004)

LMI LI

LCR AFR

x x

2001 2005

x

T

2001

PAHO (2005) METAP (2004) PAHO (2005)

x

2001

PAHO (2005)

HIC

ECA

x

2007

x

T

2001

UNSD (2009)

x

2007

UNSD (2009)

Ethiopia7

LI

AFR

x

2006

x

1995

Fiji Finland France Gabon2 Gambia2 Georgia Germany

UMI HIC HIC UMI LI UMI HIC

EAP OECD OECD AFR AFR ECA OECD

x x x x x x x

1994 2006 2006 2005 2005 2007 2006

x x x

1994 2000 2005

McIntyre (2005) UNSD (2009) OECD (2008)

x x x

2001 2007 2005

UNSD (2009) UNSD (2009) OECD (2008)

x

2008

Asase et al. (2009)

x

1997

UNSD (2009)

x x x

2006 2007 2000

UNSD (2009) UNSD (2009) UNSD (2009)

LI

AFR

x

2008

Greece Grenada Guatemala Guinea Guyana Haiti Honduras Hong Kong, China Hungary Iceland

HIC UMI LMI LI LMI LI LMI

OECD LCR LCR AFR LCR LCR LCR

x x x

2006 2001 2001

PAHO (2005) USAID (2009) European Environment Agency (2008) Tadesse et al. (2008) McIntyre (2005) OECD (2008) OECD (2008) USAID (2009) USAID (2009) UNSD (2009) OECD (2008) Asase et al. (2009) OECD (2008) PAHO (2005) PAHO (2005)

x x x

2001 2001 2001

PAHO (2005) PAHO (2005) PAHO (2005)

x x x

T T T

2001 2001 2001

PAHO (2005) PAHO (2005) PAHO (2005)

x x

2001 2001

PAHO (2005) PAHO (2005)

HIC

EAP

x

2008

Shekdar (2009)

x

T

2007

UNSD (2009)

x

2007

UNSD (2009)

x

2008

Shekdar (2009)

HIC HIC

OECD OECD

x x

2006 2006

x x

T T

2003 2007

UNSD (2009) UNSD (2009)

x x

2003 2004

OECD (2008) OECD (2008)

x x

2005 2003

OECD (2008) OECD (2008)

India

LMI

SAR

x

2006

OECD (2008) OECD (2008) Hanrahan et al. (2006)

x

2004

UNSD (2009)

Indonesia8

LMI

EAP

x

2008

Shekdar (2009)

x

U

2006

Pasang et al. (2007)

x

2000

LMI

MENA

x

2005

x

2005

LMI HIC

MENA OECD

x x

2005 2006

x x

T T

2005 2005

UNSD (2009) UNSD (2009)

x

2005

OECD (2008)

Israel

HIC

MENA

x

1996

Italy Jamaica3 Japan

HIC UMI HIC

OECD LCR OECD

x x x

Jordan

LMI

MENA

Kenya

LI

Korea, South Kuwait

Ghana

Iran, Islamic Rep.9 Iraq10 Ireland

x x

T T

2007 2007

UNSD (2009) UNSD (2009)

x x

T T

2007 2007

x

U

2008

x x x

T T T

2007 2001 2001

UNSD (2009) UNSD (2009) Asase et al. (2009) UNSD (2009) PAHO (2005) PAHO (2005)

2006 2001 2005

Damghani et al. (2008) UNESCWA (2007) OECD (2008) Israel Ministry of the Environment (2000) OECD (2008) PAHO (2005) OECD (2008)

x x x

T T T

2007 2001 2003

x

2001

METAP (2004)

x

U

2001

AFR

x

2002

HIC

OECD

x

2005

x

T

2002

HIC

MENA

x

2009

Kenya Ministry of Environment (2002) OECD (2008) Personal communication

x x

2004 2005

OECD (2008) OECD (2008)

x

2004

x

2008

x x x

2003 2001 2001

OECD (2008) Asase et al. (2009) OECD (2008) PAHO (2005) PAHO (2005)

x

2005

x

1996

UNSD (2009) PAHO (2005) UNSD (2009) METAP (2004)

x x x

UNSD (2009)

Ngoc and Schnitzer (2009) Damghani et al. (2008)

x

2005

UNSD (2009)

2005 2001 2003

OECD (2008) Israel Ministry of the Environment (2000) OECD (2008) PAHO (2005) OECD (2008)

x x x

2005 2007 2008

OECD (2008) UNSD (2009) Shekdar (2009)

x

2001

METAP (2004)

x

2001

METAP (2004)

x

2004

OECD (2008)

x

2005

OECD (2008)

42


ANNEX C (continued)

Availability of MSW Data by Country Region

LI

EAP

x

2008

Shekdar (2009)

Latvia

UMI

ECA

x

2007

European Environment Agency (2008)

x

T

1999

UNSD (2009)

x

2003

Lebanon

UMI

MENA

x

2000

METAP (2004)

x

U

2000

METAP (2004)

x

2000

Lesotho Liberia11

LMI LI

AFR AFR

x

2005

USAID (2009)

Lao PDR

2


Source

UMI

ECA

x

2007

Luxembourg Macao, China

HIC HIC

OECD EAP

x x

2006 2003

Macedonia, FYR

LMI

ECA

x

2006

LI LI

AFR AFR

x x

2003 2005

Malaysia12

UMI

EAP

x

2002

Maldives

LMI

SAR

x

1998

LI

AFR

x

2007

Malta

HIC

MENA

x

2007

Marshall Islands Mauritania2 Mauritius Mexico Monaco

LMI LI UMI UMI HIC

EAP AFR AFR LCR OECD

x x x

2005 2003 2006

USAID (2009) Payet (2003) OECD (2008)

Mali

13

Year of Data

Source


Lithuania

Madagascar Malawi2

Collection

Urban or Total

Income Level

Country

OECD (2008) Jin et al. (2006) Hristovski et al. (2007) Payet (2003) USAID (2009) Saeed et al. (2009) UNEP (2002) Samake et al. (2009) European Environment Agency (2008)

x x

x

T T

T

2007 2007

2007

UNSD (2009) UNSD (2009)

UNSD (2009)

Disposal

Year of Data

x

2003

x x

2003 2007

x

2007

Composition

Year of Data

x

2000

Latvia Ministry of Environment (2006)

x

2003

METAP (2004)

x

2000

METAP (2004)

x

2004

UNEP (2007)

x x

2005 2007

OECD (2008) UNSD (2009)

x

1996

Macedonia (1996)

x

2007

UNSD (2009)

x

2000


x

1995

UNSD (2009)

Source

Lithuania Ministry of Environment (2005) OECD (2008) UNSD (2009)

UNSD (2009)

Source Ngoc and Schnitzer (2009) Latvia Ministry of Environment (2006)

x

T

x

T

2007

UNSD (2009)

x

2007

UNSD (2009)

x

T

2007

UNSD (2009)

x

2007

UNSD (2009)

x

2007

UNSD (2009)

x x x

T T T

2007

UNSD (2009)

UNSD (2009) OECD (2008) UNSD (2009)

2007 2005

UNSD (2009) OECD (2008)

UNSD (2009)

2007 2006 2007

x x

2007

x x x

x

T

2002

METAP (2004)

x

2002

METAP (2004)

x

2000

UNSD (2009) Grest (2008) Ngoc and Schnitzer (2009)

Mongolia

LI

EAP

x

2001

Mongolia Ministry of Nature (2001)

Morocco

LMI

MENA

x

2002

METAP (2004)

LI

AFR

x

2007

Grest (2008)

x

2007

x

2000

x

2008

Shekdar (2009)

x x

2004 1995

OECD (2008) UNSD (2009)

x x x

2005 2008 2005

UNSD (2009) Imam et al. (2008) OECD (2008)

x

2009

x

2000

Mozambique

14

Myanmar

UMI

EAP

x

2000

IPCC (2006)

Namibia2

UMI

AFR

x

2005

LI

SAR

x

2008

Netherlands New Zealand Nicaragua3 Niger2 Nigeria Norway Oman

HIC HIC LMI LI LMI HIC HIC

OECD OECD LCR AFR AFR OECD MENA

x x x x x x x

2006 2006 2001 2005 2008 2006 1997

Pakistan15

LMI

SAR

x

2009

Panama Paraguay Peru

UMI LMI UMI

LCR LCR LCR

x x x

2001 2001 2001

USAID (2009) Shekdar (2009) per cap OECD (2008) OECD (2008) PAHO (2005) USAID (2009) Solomon (2009) OECD (2008) Al-Yousfi Batool and Nawaz (2009) PAHO (2005) PAHO (2005) PAHO (2005)

Philippines

LMI

EAP

x

2008

Shekdar (2009)

Poland

UMI

ECA

x

2007

Portugal Qatar

HIC HIC

OECD MENA

x x

2006 2004

Nepal

European Environment Agency (2008) OECD (2008) UNESCWA (2007)

x

U

2003

x

T

2007

Alam et al. (2008) UNSD (2009)

x

T

2001

PAHO (2005)

x

x x x

x

T

T T T

T

2004

2001 2001 2001

2007

UNSD (2009)

PAHO (2005) PAHO (2005) PAHO (2005)

UNSD (2009)

x x x x

2004 1999 2001 2005

OECD (2008) OECD (2008) PAHO (2005) UNSD (2009)

x

2004

OECD (2008)

x x x

2001 2001 2001

PAHO (2005) PAHO (2005) PAHO (2005)

x

2001

x

2000

Batool and Nawaz (2009) UNSD (2009) UNSD (2009) Ngoc and Schnitzer (2009)

x

2005

OECD (2008)

x

1990

UNSD (2009)

x

2005

OECD (2008)

x

2001

OECD (2008)

ANNEX

43

ANNEX C (continued)

Availability of MSW Data by Country Country

Romania Russian Federation Rwanda2 Sao Tome and Principe2 Saudi Arabia Senegal2

Source

Collection

Urban or Total

Year of Data

Source

Disposal

Year of Data

Source

Composition

Year of Data

Source

2007


x

T

2002

UNSD (2009)

x

2007

UNSD (2009)

x

2006

Atudorei

x

2000

IPCC (2006)

UNSD (2009) Denmark Ministry of Foreign Affairs Payet (2003) Patriotic Vanguard (2007)

x

2007

UNSD (2009)

x

1999

UNSD (2009)

x

2007

Patriotic Vanguard (2007)

Income Level

Region


UMI

ECA

x

UMI

ECA

LI

AFR

x

2005

USAID (2009)

LMI

AFR

x

2005

USAID (2009)

HIC LI

MENA AFR

x x

1997 2005

Al-Yousfi USAID (2009)

x

T

2005

x

T

2006

LI

ECA

x

2006

Denmark Ministry of Foreign Affairs

UMI

AFR

x

2003

Payet (2003)

x

T

2003

Sierra Leone16

LI

AFR

x

2007

Patriotic Vanguard (2007)

x

U

2007

Singapore

HIC

EAP

x

2008

x

T

2007

UNSD (2009)

x

2007

UNSD (2009)

x

2000

Slovak Republic

HIC

OECD

x

2006

x

T

2007

UNSD (2009)

x

2005

x

2002

Slovenia

HIC

ECA

x

2007

x

T

2002

UNSD (2009)

x

2003

OECD (2008) Slovenia Ministry of the Environment (2006)

Ngoc and Schnitzer (2009) OECD (2008)

x

1994

McIntyre (2005)

x x

2002 2008

OECD (2008) Shekdar (2009)

x

2002

UNSD (2009)

Serbia1 Seychelles

Singapore (2009) OECD (2008) European Environment Agency (2008)

Solomon Islands

LMI

EAP

x

1994

South Africa17

UMI

AFR

x

2003

Spain Sri Lanka St. Kitts and Nevis3 St. Lucia St. Vincent and the Grenadines Sudan Suriname Swaziland2 Sweden Switzerland Syrian Arab Republic Tajikistan

HIC LMI

OECD SAR

x x

2006 2003

McIntyre (2005) City of Cape Town (2008) OECD (2008) Perera (2003)

UMI

LCR

x

2001

PAHO (2005)

x

T

2001

UMI

LCR

x

2001

PAHO (2005)

x

T

x

x

2004

OECD (2008)

PAHO (2005)

x

2001

PAHO (2005)

2001

PAHO (2005)

x

2001

PAHO (2005)

T

2001

PAHO (2005)

x

2001

PAHO (2005)

x

T

2001

PAHO (2005)

x

2001

PAHO (2005)

x x

T T

2007 2007

x x

2005 2005

OECD (2008) OECD (2008)

x x

2005 2005

OECD (2008) OECD (2008)

x

U

2002

UNSD (2009) UNSD (2009) METAP (2004)

x

2002

METAP (2004)

x

2002

METAP (2004)

x

U

2005

x

2000

UNSD (2009)

x

2000

x x

2007 1994

Ngoc and Schnitzer (2009) UNSD (2009) McIntyre (2005)

UMI

LCR

x

2001

PAHO (2005)

LMI UMI LMI HIC HIC

AFR LCR AFR OECD OECD

x x x x x

2000 2001 2005 2006 2006

IPCC (2006) PAHO (2005) USAID (2009) OECD (2008) OECD (2008)

LMI

MENA

x

2002

METAP (2004)

UMI

ECA

x

2001

Tanzania

LI

AFR

x

2005

CEROI (2001) Kassim and Ali (2006)

Thailand

LMI

EAP

x

2008

Shekdar (2009)

Togo2 Tonga Trinidad and Tobago

LI LMI

AFR EAP

x x

2005 1994

USAID (2009) McIntyre (2005)

HIC

LCR

x

2001

PAHO (2005)

x

T

2001

Tunisia

LMI

MENA

x

2000

METAP (2004)

x

U

2000

Turkey

LMI

ECA

x

2006

OECD (2008)

x

T

2004

x

U

2002

Turkmenistan

Uganda18 United Arab Emirates3

LMI

ECA

x

2000

LI

AFR

x

2004

Turkmenistan Ministry of Nature Protection (2000) Bingh (2004)

HIC

MENA

x

2000

UNESCWA (2007)

Kassim and Ali (2006)

PAHO (2005) METAP (2004) Turan et al. (2009)

Bingh (2004)

x

2001

PAHO (2005)

x

2003

UNSD (2009)

x

2000

METAP (2004)

x

2000

METAP (2004)

x

2004

Turan et al. (2009)

x

2008

Turan et al. (2009)

x

2006

UNSD (2009)

x

2002

Bingh (2004)

44


ANNEX C (continued)

Availability of MSW Data by Country Country

Income Level

Region

United Kingdom United States Uruguay Vanuatu Venezuela, RB

HIC HIC UMI LI UMI

OECD OECD LCR EAP LCR

x x x x x

2006 2006 2001 1994 2001

LI

EAP

x

2004

LMI

MENA

x

2001

METAP (2004)

Zambia19

LI

AFR

x

Zimbabwe2

LI

AFR

x

2005

Environmental Council of Zambia (2004) USAID (2009)

Vietnam West Bank and Gaza


Source OECD (2008) OECD (2008) PAHO (2005) McIntyre (2005) PAHO (2005) World Bank (2004)

Collection

Urban or Total

Year of Data

Source

Disposal

Year of Data

Source

x x x

T T T

2007 2005 2001

UNSD (2009) UNSD (2009) PAHO (2005)

x x x

2005 2005 2001

OECD (2008) OECD (2008) PAHO (2005)

x

T

2001

PAHO (2005)

x

2001

PAHO (2005)

x

U

2001

METAP (2004)

x

T

2005

UNSD (2009)

NOTES: 1 Year for generation data is assumed to be 2006 2 Generation rates calculated using a per capita rate of 0.5kg/cap/day 3 Generation value refers to domestic waste (household) only 4 Generation rates are for urban areas only 5 Collection and disposal values are for Pnom Penh only 6 Generation rate is for Dalmatia 7 Genearation value for Mekelle City 8 Collection value is for Jakarta only 9 Generation and composition values are for Tehran 10 Population values are for 1999, the most recent year available 11 Composition values for Monrovia only 12 Generation values are for Kuala Lumpur 13 Generation and composition values are for Bamako 14 Generation and composition values are for Maputo 15 Generation and composition values are for Lahore 16 All values are for Freetown 17 Generation values are based on Cape Town per capita values 18 All values are for Kampala city only 19 Generation values are from 1996; composition values are for Lusaka only

x

2001

METAP (2004)

Composition

Year of Data

Source

x x x

2005 2003 1994

OECD (2008) UNSD (2009) McIntyre (2005)

x

2000


x

2001

METAP (2004)

x

2007

UNSD (2009)

ANNEX

ANNEX D

Countries Excluded for Lack of Data Country Afghanistan American Samoa Aruba Azerbaijan Bermuda Bosnia and Herzegovina Cayman Islands Channel Islands Djibouti Equatorial Guinea Faeroe Islands French Polynesia Greenland Guam Guinea-Bissau Isle of Man Kazakhstan Kiribati Korea, Dem. People’s Rep. Kosovo Kyrgyz Republic Libya Liechtenstein Mayotte Micronesia, Federated States of Moldova Montenegro Netherlands Antilles New Caledonia Northern Mariana Islands Palau Papua New Guinea Puerto Rico Samoa San Marino Somalia Taiwan, China Timor-Leste Ukraine Uzbekistan Virgin Islands (US) Yemen, Republic of

Income level

Region

LI UMI HIC LMI HIC UMI HIC HIC LMI HIC HIC HIC HIC HIC LI HIC UMI LMI LI LMI LI UMI HIC UMI LMI LMI UMI HIC HIC HIC LMI LMI HIC LMI HIC LI HIC LMI LMI LI HIC LI

SAR EAP OECD ECA OECD ECA OECD OECD MENA OECD OECD OECD OECD OECD AFR OECD ECA EAP EAP ECA ECA MENA OECD AFR EAP ECA ECA OECD OECD OECD EAP EAP OECD EAP OECD AFR EAP EAP ECA ECA OECD MENA

45

46


ANNEX E

Estimated Solid Waste Management Costs Estimated Solid Waste Management Costs by Disposal Method 1 Low Income Countries

Lower Mid Inc Countries

Upper Mid Inc Countries

High Income Countries

$10,725

0.22

0.29

0.42

0.78

43%

68%

85%

98%

Income (GNI/capita) Waste Generation (tonnes/capita/yr) Collection Efficiency (percent collected)

Cost of Collection and Disposal (US$/tonne)

Collection2 Sanitary Landfill Open Dumping Composting3 Waste -to-Energy Incineration4 Anaerobic Digestion5

20-50 10-30 2-8 5-30

30-75 15-40 3-10 10-40

40-90 25-65 NA 20-75

85-250 40-100 NA 35-90

NA

40-100

60-150

70-200

NA

20-80

50-100

65-150

NOTE: This is a compilation table from several World Bank documents, discussions with the World Bank’s Thematic Group on Solid Waste, Carl Bartone and other industry and organizational colleagues. Costs associated with uncollected waste—more than half of all waste generated in low-income countries—are not included.

Estimated Solid Waste Management Costs 2010 and 2025 Country Income Group Low Income Countries

7

Lower Middle Income Countries

8

2010 Cost6

2025 Cost

$1.5 billion

$7.7 billion

$20.1 billion

$84.1 billion

Upper Middle Income Countries

$24.5 billion

$63.5 billion

High Income Countries10

$159.3 billion

$220.2 billion

Total Global Cost (US$)

$205.4 billion

9

$375 billion

Source: Authors’ calculations with input from What a Waste report (Hoornweg and Thomas 1999) and the World Bank Solid Waste Thematic Group and Carl Bartone.

All values provided in the table are exclusive of any potential carbon finance, subsidies, or external incentives. Costs included are for purchase (including land), operation, maintenance, and debt service.

1

2

Collection includes pick up, transfer, and transport to final disposal site for residential and non-residential waste.

3

Composting excludes sale of finished compost (which ranges from $0 to $100/ton).

4

Includes sale of any net energy; excludes disposal costs of bottom and fly ash (non hazardous and hazardous).

5

Anaerobic digestion includes sale of energy from methane and excludes cost of residue sale and disposal.

6

Cost of SWM (US$) = waste generated (tonnes) X percent of waste collected (%) X [cost of collection ($/ton) + cost of disposal ($/ton)]

7

2010: $1.5bil = 75,000,000 tonnes X 43% X ($30/ton + $15/ton); 2025: $7.7bil = 201,000,000 tonnes X 55% X ($45/ton + $25/ton)

8

2010: $20.1bil = 369,000,000 tonnes X 68% X ($50/ton +$30/ton); 2025: $84.1bil = 956,000,000 tonnes X 80% X ($65/ton + $45/ton)

2010: $24.5bil = 243,000,000 tonnes X 84% X ((0.9Landfill ($65/ton + $50/ton)) + (0.1Incinerate ($65/ton + $100/ton))); 2025: $63.5bil = 426,000,000 X 92% X ((0.85Landfill ($85/ton + $65/ton)) + (0.15Incinerate($85/ton +$145/ton)))

9

10 2010: $159.3bil = 602,000,000 tonnes X 98% X ((0.8Landfill ($180/ton + $75/ton)) + (0.2 Incinerate ($180/ton + $150/ton))); 2025: $220.2bil = 686,000,000 tonnes X 98% X ((0.75Landfill ($210/ton + $95/ton)) + 0.25Incinerate($210/ton + $185/ton)))

ANNEX

ANNEX F

MSW Generation Data for Cities Over 100,000 City

Year

Urban Population

Generation Rate (kg/capita/day)

Total MSW Generated (kg/day)

Total Waste (tons/day)

Africa Benin Parakou (UNSD 2009) Porto Novo (Achankeng 2003) Burkina Faso (UNSD 2009) Ouagadougou Burundi (Achankeng 2003) Bujumbura Cameroon (Achankeng 2003) Douala Yaounde Congo, Rep. (Achankeng 2003) Brazzaville Cote d’Ivoire (Achankeng 2003) Abidjan Egypt (Achankeng 2003) Cairo Gambia, The (Achankeng 2003) Banjul Ghana Accra (Achankeng 2003) Kumasi (Asase 2009) Guinea (UNSD 2009) Conakry Madagascar (Achankeng 2003) Antananarivo Mauritania (Achankeng 2003) Nouakchott Morocco (Achankeng 2003) Rabat Namibia (Achankeng 2003) Windhoek Niger Niamey (Achankeng 2003) Zinder (UNSD 2009) Nigeria (Achankeng 2003) Ibadan Lagos Rwanda (Achankeng 2003) Kigali Senegal (Achankeng 2003) Dakar Tanzania (Achankeng 2003) Dar es Salaam Togo (Achankeng 2003) Lome Tunisia (Achankeng 2003) Tunis Uganda (Achankeng 2003) Kampala Zambia (UNSD 2009) Lusaka Zimbabwe (UNSD 2009) Harare

2002 1993

148,450

0.59 0.50

87,671

88 —

2002

876,200

0.79

692,635

693

1993

1.40

1993

0.70

1993

0.80

1993

0.60

1993

1.00

1993

0.50

1993

0.30

—

1993 2006

1,610,867

0.40 0.60

966,520

967

2007

3,000,000

0.24

725,274

725

69,430

69

1993

0.30

1993

0.90

1993

0.60

1993

0.70

1993 2006

242,800a

1.00 0.29

1993 1993

1.10 0.30

1993

0.60

1993

0.70

1993

1.00

1993

1.90

1993

0.50

1993

6.00

2005

1,300,000

0.90

1,171,994

1,172

2005

2,500,000

0.08

207,500

208

47

48


ANNEX F (continued)


Year

Urban Population




East Asia & Pacific China** (Hoornweg et al. 2005) Anshan, Liaoning Baotou, Inner Mongolia Beijing, Beijing Benxi, Liaoning Changchun, Jilin Changde, Hunan Changsha, Hunan Changzhou, Jiangsu Chengdu, Sichuan Chifeng, Inner Mongolia Chongqing, Chongqing Dalian, Liaoning Daqing, Heilongjiang Datong, Shanxi Dongguan, Guangdong Fushun, Guangdong Fuxin, Liaoning Fuyu, Jilin Fuzhou, Fujian Guangzhou, Guangdong Guiyang, Guizhou Handan, Hebei Hangzhou, Zhejiang Harbin, Heilongjiang Hefei, Anhui Hengyang, Hunan Heze, Shandong Huaian, Jiangsu Huaibei, Anhui Huainan, Anhui Huhehaote, Inner Mongolia Hunjiang, Jilin Huzhou, Zhejiang Jiamusi, Heilongjiang Jiaxing, Zhejiang Jilin, Jilin Jinan, Shandong Jingmen, Hubei Jining, Inner Mongolia Jinzhou, Liaoning Jixi, Liaoning Kaifeng, Henan Kunming, Yunnan Lanzhou, Gansu Leshan, Sichuan Linqing, Shandong Linyi, Shandong Liuan, Anhui Liupanshui, Guizhou Luoyang, Henan Mianyang, Sichuan Mudanjiang, Heilongjiang

2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000

1,453,000 1,319,000 10,839,000 957,000 3,093,000 1,374,000 1,775,000 886,000 3,294,000 1,087,000 4,900,000 2,628,000 1,076,000 1,165,000 1,319,000 1,413,000 785,000 1,025,000 1,397,000 3,893,000 2,533,000 1,996,000 1,780,000 2,928,000 1,242,000 799,000 1,600,000 1,232,000 814,000 1,354,000 978,000 772,000 1,077,000 874,000 791,000 1,435,000 2,568,000 1,153,000 1,019,000 834,000 949,000 769,000 1,701,000 1,730,000 1,137,000 891,000 2,498,000 1,818,000 2,023,000 1,451,000 1,065,000 801,000

0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

1,307,701 1,187,101 9,755,101 861,301 2,783,701 1,236,600 1,597,501 797,400 2,964,600 978,301 4,410,000 2,365,200 968,400 1,048,501 1,187,101 1,271,701 706,501 922,501 1,257,301 3,503,701 2,279,701 1,796,400 1,602,000 2,635,200 1,117,800 719,101 1,440,000 1,108,800 732,600 1,218,600 880,200 694,800 969,301 786,600 711,901 1,291,501 2,311,200 1,037,701 917,101 750,600 854,101 692,101 1,530,901 1,557,000 1,023,301 801,901 2,248,200 1,636,200 1,820,701 1,305,901 958,501 720,901

1,308 1,187 9,755 861 2,784 1,237 1,598 797 2,965 978 4,410 2,365 968 1,049 1,187 1,272 707 923 1,257 3,504 2,280 1,796 1,602 2,635 1,118 719 1,440 1,109 733 1,219 880 695 969 787 712 1,292 2,311 1,038 917 751 854 692 1,531 1,557 1,023 802 2,248 1,636 1,821 1,306 959 721

ANNEX

ANNEX F (continued)


Year

Nanchang, Jiangxi Nanjing, Jiangsu Neijiang, Sichuan Ningbo, Zhejiang Pingxiang, Jiangxi Qingdao, Shandong Qiqihar, Heilongjiang Shanghai, Shanghai Shantou, Guangdong Shenyang, Liaoning Shenzhen, Guangdong Shijianzhuang, Hebei Suining, Sichuan Suqian, Jiangsu Suzhou, Jiangsu Taian, Shandong Taiyuan, Shanxi Tangshan, Hebei Tianjin, Tianjin Tianmen, Hubei Tianshui, Gansu Tongliao, Jilin Wanxian, Chongqing Weifang, Shandong Wenzhou, Zhejiang Wuhan, Hubei Wulumuqi, Xinjiang Wuxi, Jiangsu Xian, Shaanxi Xiangxiang, Hunan Xiantao, Hubei Xianyang, Shaanxi Xiaoshan, Zhejiang Xinghua, Jiangsu Xintai, Hebei Xinyi, Jiangsu Xinyu, Guangdong Xuanzhou, Anhui Xuzhou, Jiangsu Yancheng, Jiangsu Yichun, Jiangxi Yichun, Jilin Yixing, Jiangsu Yiyang, Hunan Yongzhou, Hunan Yueyang, Hunan Yulin, Guangxi Yuyao, Zhejiang Yuzhou, Henan Zaoyang, Hubei Zaozhuang, Shandong Zhangjiagang, Jiangsu Zhangjiakou, Hebei Zhanjiang, Guangdong Zhaodong, Heilongjiang

2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000

Urban Population 1,722,000 2,740,000 1,393,000 1,173,000 1,502,000 2,316,000 1,435,000 12,887,000 1,176,000 4,828,000 1,131,000 1,603,000 1,428,000 1,189,000 1,183,000 1,503,000 2,415,000 1,671,000 9,156,000 1,779,000 1,187,000 785,000 1,759,000 1,287,000 1,269,000 5,169,000 1,415,000 1,127,000 3,123,000 908,000 1,614,000 896,000 1,124,000 1,556,000 1,325,000 973,000 808,000 823,000 1,636,000 1,562,000 871,000 904,000 1,108,000 1,343,000 1,097,000 1,213,000 1,558,000 848,000 1,173,000 1,121,000 2,048,000 886,000 880,000 1,368,000 851,000

Generation Rate (kg/capita/day) 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Total MSW Generated (kg/day) 1,549,800 2,466,000 1,253,701 1,055,701 1,351,800 2,084,400 1,291,501 11,598,301 1,058,400 4,345,200 1,017,901 1,442,701 1,285,200 1,070,101 1,064,701 1,352,701 2,173,501 1,503,901 8,240,400 1,601,101 1,068,301 706,501 1,583,101 1,158,301 1,142,101 4,652,101 1,273,501 1,014,301 2,810,701 817,200 1,452,600 806,400 1,011,600 1,400,400 1,192,501 875,701 727,200 740,701 1,472,400 1,405,800 783,901 813,600 997,200 1,208,701 987,301 1,091,701 1,402,200 763,200 1,055,701 1,008,901 1,843,200 797,400 792,000 1,231,200 765,901

Total Waste (tons/day) 1,550 2,466 1,254 1,056 1,352 2,084 1,292 11,598 1,058 4,345 1,018 1,443 1,285 1,070 1,065 1,353 2,174 1,504 8,240 1,601 1,068 707 1,583 1,158 1,142 4,652 1,274 1,014 2,811 817 1,453 806 1,012 1,400 1,193 876 727 741 1,472 1,406 784 814 997 1,209 987 1,092 1,402 763 1,056 1,009 1,843 797 792 1,231 766

49

50


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Zhengzhou, Henan Zibo, Shandong Zigong, Sichuan China, Hong Kong SAR (UNSD 2009) Hong Kong China, Macao SAR (UNSD 2009) Macao Indonesia (UNSD 2009) Jakarta Philippines (UNSD 2009) Manila Quezon City Albania Tirana Belarus Minsk Croatia Zagreb Georgia Batumi Kutaisi Tbilisi Argentina Area Metropolitana Buenos Aires Bahia Blanca Neuquen Salta Capital Bahamas Nassau, Bahamas Barbados* Barbados Bolivia* Cochabamba El Alto La Paz Oruro Potosi Santa Cruz de la Sierra Sucre Tarija Brazil Abaetetuba Aguas Lindas de Goias Alagoinhas Alvorada Americana Ananindeua Anapolis Angra dos Reis Aparaceida de Goiania Apucarana Aracaju Aracatuba

Year

Urban Population




2000 2000 2000

2,070,000 2,675,000 1,072,000

0.90 0.90 0.90

1,863,000 2,407,501 964,800

1,863 2,408 965

2007

6,926,000

2.47

17,128,767

17,129

2007

525,760

1.51

792,932

793

2005

8,962,000

0.88

7,896,024

7,896

4,974,766 3,728,911

4,975 3,729

2007 1,660,714 3.00 2005 2,392,701 1.56 Eastern Europe & Central Asia (UNSD 2009) 2007

1,532,000

1.01

1,549,467

1,549

2007

1,806,200

1.21

2,181,918

2,182

2006

784,900

1.24

974,904

975

605,391 568,133 1,064,384

605 568 1,064

2007 303,200 2.00 2007 185,960 3.06 2007 1,300,000 0.82 Latin America and the Caribbean (PAHO 2005) 2001 2001 2001 2001

12,544,018 285,000 202,518 472,971

1.16 0.88 0.95 0.49

14,551,061 249,660 192,392 232,040

14,551 250 192 232

2001

200,000

2.67

534,000

534

2001

268,792

0.95

255,352

255

2001 2001 2001 2001 2001 2001 2001 2001

717,026 629,955 790,353 201,230 135,783 1,113,000 193,876 135,783

0.60 0.36 0.53 0.33 0.33 0.54 0.40 0.46

430,216 226,784 419,677 66,406 45,352 599,907 77,357 62,868

430 227 420 66 45 600 77 63

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

119,152 105,746 130,095 183,968 182,593 393,569 288,085 119,247 336,392 107,827 461,534 169,254

0.29 0.44 0.58 1.14 0.95 1.27 0.62 0.75 0.30 0.88 0.89 0.74

35,000 47,000 76,000 210,000 173,900 500,000 180,000 89,200 102,000 95,000 410,000 125,000

35 47 76 210 174 500 180 89 102 95 410 125

ANNEX

ANNEX F (continued)


Year

Araguaina Araguari Arapiraca Araraquara Araras Atibaia Bage Barbacena Barra Mansa Barreiras Barretos Barueri Bauru Belem Belford Roxo Belo Horizonte Betim Blumenau Boa Vista Botucatu Braganca Paulista Brasilia Cabo de Santo Agostinho Cabo Frio Cachoeirinha Cachoeiro de Itapemirim Camacari Camaragibe Campina Grande Campinas Campo Grande Campos dos Goytacazes Canoas Carapicuiba Cariacica Caruaru Cascavel Castanhal Catanduva Caucaia Caxias Caxias do Sul Chapeco Colatina Colombo Contagem Cotia Crato Criciuma Cubatao Curitiba Diadema Dourados

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

Urban Population 113,143 101,974 186,466 182,471 104,196 111,300 118,767 114,126 170,753 131,849 103,913 208,281 316,064 1,280,614 434,474 2,238,526 306,675 261,808 200,568 108,306 125,031 2,051,146 152,977 126,828 107,564 174,879 161,727 128,702 355,331 969,396 663,621 406,989 306,093 344,596 324,285 253,634 245,369 134,496 105,847 250,479 139,756 360,419 146,967 112,711 183,329 538,017 148,987 104,646 170,420 108,309 1,587,315 357,064 164,949

Generation Rate (kg/capita/day) 0.53 0.88 0.99 0.87 0.72 1.49 0.42 0.83 0.76 1.76 0.76 1.87 1.39 1.57 0.81 1.43 0.49 0.84 0.57 1.41 1.03 0.76 0.92 1.58 1.17 1.03 0.99 1.01 1.35 1.69 0.75 0.73 0.68 0.73 1.05 0.79 0.59 0.40 0.94 0.73 0.76 0.92 0.49 0.71 0.39 1.86 0.78 0.33 0.56 0.85 0.75 0.79 1.33

Total MSW Generated (kg/day) 59,500 90,000 185,000 158,400 75,000 165,700 50,000 95,200 130,000 232,200 79,200 390,000 440,000 2,012,000 350,000 3,201,800 150,000 220,000 115,000 153,000 128,500 1,556,700 140,000 200,000 125,400 180,000 160,000 130,000 480,000 1,641,000 496,400 296,000 207,000 250,000 340,000 200,000 145,000 54,000 100,000 183,000 106,600 330,000 72,200 80,000 72,000 1,000,000 116,700 35,000 96,000 92,000 1,186,700 281,600 219,000

Total Waste (tons/day) 60 90 185 158 75 166 50 95 130 232 79 390 440 2,012 350 3,202 150 220 115 153 129 1,557 140 200 125 180 160 130 480 1,641 496 296 207 250 340 200 145 54 100 183 107 330 72 80 72 1,000 117 35 96 92 1,187 282 219

51

52


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Duque de Caxias Embu Feira de Santana Ferraz de Vasconcelos Florianopolis Fortaleza Foz do Iguacu Franca Francisco Morato Franco da Rocha Garanhuns Goiania Governador Valadares Gravatai Guarapuava Guaratingueta Guaruja Guarulhos Hortolandia Ibirite Ilheus Imperatriz Indaiatuba Ipatinga Itaborai Itabuna Itajai Itapecerica da Serra Itapetininga Itapevi Itaquaquecetuba Itu Jaboatao dos Guararapes Jacarei Jaragua do Sul Jau Jequie Ji-Parana Joao Pessoa Joinville Juazeiro Juazeiro do Norte Juiz de For a Jundiai Lages Lauro de Freitas Limeira Linhares Londrina Luziania Macae Macapa Maceio

Year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

Urban Population 775,456 207,663 480,949 142,377 342,315 2,141,402 258,543 287,737 133,738 108,122 117,749 1,093,007 247,131 232,629 155,161 104,219 264,812 1,072,717 152,523 133,044 222,127 230,566 147,050 212,496 187,479 196,675 147,494 129,685 125,559 162,433 272,942 135,366 581,556 191,291 108,489 112,104 147,202 106,800 597,934 429,604 174,567 212,133 456,796 323,397 157,682 113,543 249,046 112,617 447,065 141,082 132,461 283,308 797,759

Generation Rate (kg/capita/day) 0.94 0.67 1.56 0.58 1.27 1.11 0.75 0.95 0.82 0.59 1.66 1.17 1.21 0.55 0.53 0.58 0.98 0.79 0.62 0.83 0.36 0.98 0.61 0.94 0.62 1.27 0.95 0.66 0.50 0.60 0.70 0.96 0.77 0.63 0.72 1.03 0.48 0.66 1.72 1.15 1.18 1.08 0.64 1.02 0.51 0.79 0.64 0.57 1.61 0.71 1.89 1.34 1.32

Total MSW Generated (kg/day) 730,000 140,000 750,800 83,000 435,000 2,375,000 195,000 273,000 109,100 64,000 195,000 1,279,700 300,000 127,100 83,000 60,000 260,600 850,000 95,000 110,000 80,000 227,000 90,400 200,000 116,000 250,000 140,000 85,500 62,200 98,000 190,000 130,000 450,000 120,000 78,000 115,400 70,000 70,000 1,027,900 493,200 206,000 230,000 290,500 330,200 80,000 90,000 159,500 64,000 720,000 100,000 250,000 380,000 1,050,000

Total Waste (tons/day) 730 140 751 83 435 2,375 195 273 109 64 195 1,280 300 127 83 60 261 850 95 110 80 227 90 200 116 250 140 86 62 98 190 130 450 120 78 115 70 70 1,028 493 206 230 291 330 80 90 160 64 720 100 250 380 1,050

ANNEX

ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Mage Manaus Maraba Maracanau Marilia Maringa Maua Mogi Guacu Moji das Cruzes Montes Claros Mossoro Natal Nilopolis Niteroi Nossa Senhora do Socorro Nova Friburgo Nova Iguacu Novo Hamburgo Olinda Osasco Palhoca Palmas Paranagua Parnaiba Parnamirim Passo Fundo Patos de Minas Paulista Pelotas Petrolina Petropolis Pindamonhangaba Pinhais Piracicaba Pocos de Caldas Ponta Grossa Porto Algre Porto Velho Pouso Alegre Praia Grande Presidente Prudente Queimados Recife Resende Ribeirao das Neves Ribeirao Pires Ribeirao Preto Rio Branco Rio Claro Rio de Janeiro Rio Grande Rio Verde Rondonopolis Sabara Salvador Santa Barbara D Oeste

Year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

Urban Population 205,830 1,405,835 168,020 179,732 197,342 288,653 363,392 124,228 330,241 306,947 213,841 712,317 153,712 459,451 131,679 173,418 920,599 236,193 367,902 652,593 102,742 137,355 127,339 132,282 124,690 168,458 123,881 262,237 323,158 218,538 286,537 126,026 102,985 329,158 135,627 273,616 1,360,590 334,661 106,776 193,582 189,186 121,993 1,422,905 104,549 246,846 104,508 504,923 253,059 168,218 5,857,904 186,544 116,552 150,227 115,352 2,443,107 170,078

Generation Rate (kg/capita/day) 1.04 1.55 0.31 0.64 0.98 0.98 0.64 0.67 0.63 1.51 0.71 1.72 1.63 1.47 0.38 0.81 0.75 0.66 1.05 0.87 0.24 0.59 1.10 0.94 0.40 0.60 0.66 0.76 0.56 0.64 1.05 0.99 0.58 0.73 0.66 1.03 0.98 0.58 0.84 0.93 0.53 0.53 0.97 0.97 0.97 1.71 0.89 0.56 0.74 1.20 1.29 0.87 0.55 0.52 1.08 0.83

Total MSW Generated (kg/day) 215,000 2,180,000 52,000 115,000 192,500 284,000 232,700 83,000 208,100 462,000 151,500 1,223,000 250,000 675,300 50,500 140,000 693,900 155,000 385,600 570,000 25,000 81,000 140,000 125,000 50,000 101,300 82,000 200,000 180,000 140,000 300,000 125,000 60,000 239,700 90,000 280,900 1,340,000 193,400 90,000 180,900 100,000 64,500 1,376,000 101,000 240,000 179,000 450,000 141,200 125,100 7,058,700 240,000 101,300 82,000 60,200 2,636,500 141,000

Total Waste (tons/day) 215 2,180 52 115 193 284 233 83 208 462 152 1,223 250 675 51 140 694 155 386 570 25 81 140 125 50 101 82 200 180 140 300 125 60 240 90 281 1,340 193 90 181 100 65 1,376 101 240 179 450 141 125 7,059 240 101 82 60 2,637 141

53

54


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Santa Cruz do Sul Santa Luzia Santa Maria Santa Rita Santarem Santo Andre Santos Sao Bernardo do Campo Sao Caetano do Sul Sao Carlos Sao Goncalo Sao Joao de Meriti Sao Jose Sao Jose de Ribamar Sao Jose do Rio Preto Sao Jose dos Campos Sao Jose dos Pinhais Sao Leopoldo Sao Luis Sao Paulo Sao Vicente Sapucaia do Sul Serra Sete Lagoas Sobral Sorocaba Sumare Suzano Taboao da Serra Taubate Teixeira de Freitas Teofilo Otoni Teresina Teresopolis Timon Uberaba Uberlandia Uruguaiana Varginha Varzea Grande Viamao Vila Velha Vitoria Vitoria da Conquista Vitoria de Santo Antao Volta Redonda Chile Antofagasta, Antofagasta Antofagasta, Calama Araucanía, Temuco B.O’Higgins, Rancagua Biobío, Chillán Biobío, Concepción Biobío, Talcahuano

Year

Urban Population




2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

107,632 184,903 243,611 115,844 262,538 649,331 417,983 703,177 140,159 192,998 891,119 449,476 173,559 107,384 358,523 539,313 204,316 193,547 870,028 10,434,252 303,551 122,751 321,181 184,871 155,276 493,468 196,723 228,690 197,644 244,165 107,486 129,424 715,360 138,081 129,692 252,051 501,214 126,936 108,998 215,298 227,429 345,965 292,304 262,494 117,609 242,063

0.51 0.49 0.66 0.65 0.51 0.99 1.10 0.81 1.43 0.69 0.70 0.69 1.18 0.47 1.03 1.23 0.69 0.52 0.85 2.00 0.96 0.59 1.12 0.78 0.89 0.92 0.91 0.58 0.84 0.67 0.88 0.40 1.48 0.83 0.33 1.55 0.90 0.79 1.03 0.58 0.77 0.95 1.08 1.32 1.36 0.66

55,000 91,300 160,000 75,000 133,700 640,000 460,000 566,700 200,000 133,300 620,000 312,000 205,000 50,000 367,900 661,600 140,000 100,000 740,000 20,855,700 290,000 73,000 358,700 145,000 138,000 455,000 180,000 133,000 167,000 162,500 95,000 52,000 1,058,900 115,000 42,200 391,000 451,600 100,000 112,000 125,000 175,000 330,000 315,000 346,000 160,000 160,000

55 91 160 75 134 640 460 567 200 133 620 312 205 50 368 662 140 100 740 20,856 290 73 359 145 138 455 180 133 167 163 95 52 1,059 115 42 391 452 100 112 125 175 330 315 346 160 160

2001 2001 2001 2001 2001 2001 2001

318,779 138,402 245,347 214,344 161,953 216,061 250,348

0.80 0.65 1.03 0.80 1.00 0.80 0.94

255,023 89,961 252,707 171,475 161,953 172,849 235,327

255 90 253 171 162 173 235

ANNEX

ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Coquimbo, Coquimbo Coquimbo, La Serena Los Lagos, Osorno Los Lagos, Puerto Montt Los Lagos, Valdivia Magallanes, Punta Arenas Maule, Curicó Maule, Talca Santiago, Cerro Navia Santiago, La Florida Santiago, La Pintana Santiago, Maipú Santiago, Providencia Santiago, Recoleta Santiago, Santiago Tarapacá, Arica Valparaíso, Valparaíso Valparaíso, Viña del Mar Colombia Armenia Barrancabermeja Barranquilla Bello Bogotá Bucaramanga Buenaventura Buga Cali Cartagena Cartago Cúcuta Dosquebradas Envigado Florencia Floridablanca Girardot Ibagué Itagüí Maicao Manizales Medellín Montería Neiva Palmira Pasto Pereira Popayán Santa Marta Sincelejo Soacha Sogamoso Soledad

Year

Urban Population




2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

163,036 160,148 145,475 175,938 140,559 120,874 120,299 203,231 148,312 365,674 190,085 468,390 120,874 148,220 200,792 185,268 275,982 286,931

0.90 0.95 1.00 1.00 0.42 0.80 1.00 0.95 1.00 1.00 0.68 1.01 1.40 1.21 1.63 0.71 1.00 0.96

146,732 152,141 145,475 175,938 59,035 96,699 120,299 193,069 148,460 365,674 129,258 472,137 169,224 179,346 327,893 131,540 275,982 275,454

147 152 145 176 59 97 120 193 148 366 129 472 169 179 328 132 276 275

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

293,000 183,000 1,276,000 353,000 6,558,000 543,000 230,000 113,000 2,181,000 854,000 129,000 644,000 166,000 145,000 116,000 232,000 117,000 403,000 246,000 115,000 345,000 1,909,000 256,000 317,000 234,000 349,000 401,000 206,000 382,000 234,000 285,000 114,000 310,000

0.58 0.60 0.80 0.49 0.72 0.55 0.65 0.61 0.77 0.87 0.44 0.46 0.40 0.31 1.04 0.50 1.02 0.63 0.62 0.60 0.72 0.81 0.60 0.80 0.66 0.61 0.58 0.67 0.72 0.51 0.88 0.38 0.60

169,940 109,800 1,020,800 172,970 4,721,760 298,650 149,500 68,930 1,679,370 742,980 56,760 296,240 66,400 44,950 120,640 116,000 119,340 253,890 152,520 69,000 248,400 1,546,290 153,600 253,600 154,440 212,890 232,580 138,020 275,040 119,340 250,800 43,320 186,000

170 110 1,021 173 4,722 299 150 69 1,679 743 57 296 66 45 121 116 119 254 153 69 248 1,546 154 254 154 213 233 138 275 119 251 43 186

55

56


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Tuluá Tunja Valledupar Villavicencio Costa Rica Alajuela Desamparados San José Cuba Bayamo Camagüey Ciego de Ávila Cienfuegos Ciudad de La Habana Guantánamo Holguín Manzanillo Matanzas Pinar del Río Sancti Spíritus Santa Clara Santiago de Cuba Tunas Ecuador* Quito Santo Domingo de los Colorados El Salvador La Libertad - Nueva San Salvador San Miguel, San Miguel San Salvador - Apopa San Salvador - Ilopango, San Salvador - Mejicanos San Salvador - Soyapango San Salvador, San Salvador Santa Ana, Santa Ana Grenada Grenada Guatemala Antigua Guatemala Guatemala Jutiapa Quetzaltenango San Benito San Pedro Carchá Guyana Georgetown Haiti Cap-Haïtien Carrefour Croix des Bouquets Delmas Dessalines

Year

Urban Population




2001 2001 2001 2001

157,000 112,000 278,000 289,000

0.75 0.79 0.85 0.51

117,750 88,480 236,300 147,390

118 88 236 147

2001 2001 2001

234,737 203,770 326,384

0.85 1.38 1.02

199,526 281,203 332,585

200 281 333

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

154,832 308,288 118,935 154,897 2,186,632 222,217 268,843 110,846 133,177 162,078 109,220 220,345 452,307 144,381

0.44 0.50 0.41 0.75 0.75 0.56 0.50 0.44 0.60 0.60 0.58 0.58 0.50 0.47

67,662 154,144 48,763 116,173 1,639,974 124,442 134,422 48,440 79,906 97,247 63,348 127,800 226,154 67,859

68 154 49 116 1,640 124 134 48 80 97 63 128 226 68

2001 2001

1,841,200 200,421

0.72 0.65

1,325,664 130,274

1,326 130

2001 2001 2001 2001 2001 2001 2001 2001

136,909 172,203 139,802 115,358 172,548 285,286 479,605 167,975

0.70 0.82 0.54 0.51 0.61 0.57 0.81 0.63

95,836 141,206 75,493 58,833 105,254 162,613 388,480 105,824

96 141 75 59 105 163 388 106

2001

95,551

0.85

81,218

81

2001 2001 2001 2001 2001 2001

248,019 2,541,581 130,000 122,157 366,735 130,118

1.20 0.95 0.90 0.90 0.80 0.85

297,623 2,414,502 117,000 109,941 293,388 110,600

298 2,415 117 110 293 111

2001

180,000

1.53

275,400

275

2001 2001 2001 2001 2001

141,061 416,301 143,803 335,866 167,599

0.60 0.60 0.30 0.60 0.30

84,637 249,781 43,141 201,520 50,280

85 250 43 202 50

ANNEX

ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Gonaïves Jacmel Jean Rabel Léogâne Les Cayes Pétion Ville Petit Goâve Petite Rivière de l’Artibonite Port de Paix Port-au-Prince Saint Marc Saint Michel Honduras Choloma Distrito Central La Ceiba San Pedro Sula Jamaica* North Eastern Wasteshed( Portland, St.Mary and St.Ann) Portmore Retirement(Westmoreland, Hanover,Trelawny & St.James) Riverton ( Kgn, St.And, St.Cath. Clarendon and St.Thomas) Southern(Manchester, St.Elizabeth) Mexico Acapulco, Guerrero Acuña, Coahuila Aguascalientes, Aguascalientes Altamira, Tamaulipas Apatzingan, Michoacán Apodaca, Nuevo León Atizapan de Zaragoza, México Atlixco, Puebla Boca del Río, Veracruz Campeche, Campeche Cancún, Benito Juárez, Quintana Roo Cárdenas, Tabasco Carmen, Campeche Celaya, Guanajuato Chalco, México Chetumal, Othon P. Blanco, Quintana Roo Chihuahua, Chihuahua Chilpancingo, Guerrero Coatzacoalcos, Veracruz Colima, Colima Comitán de Domínguez, Chiapas Córdoba, Veracruz Cuauhtemoc, Chihuahua Cuautla, Morelos Cuernavaca, Morelos Culiacán, Sinaloa Delicias, Chihuahua Dolores Hidalgo, Guanajuato

Year

Urban Population




2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

138,480 138,504 121,221 105,806 152,845 143,452 125,433 126,474 113,191 1,100,085 164,868 124,603

0.30 0.60 0.30 0.25 0.30 0.60 0.25 0.35 0.40 0.60 0.30 0.30

41,544 83,102 36,366 26,452 45,854 86,071 31,358 44,266 45,276 660,051 49,460 37,381

42 83 36 26 46 86 31 44 45 660 49 37

2001 2001 2001 2001

126,402 819,867 126,721 483,384

0.70 0.67 0.63 0.69

88,481 549,311 79,834 333,535

88 549 80 334

2001

357,265

1.00

357,265

357

2001 2001

159,974 452,724

0.89 1.00

142,377 452,724

142 453

2001

1,458,155

1.00

1,458,155

1,458

2001

331,190

1.00

331,190

331

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

728,010 117,271 656,245 130,425 108,466 297,776 475,683 117,929 135,875 219,281 444,870 219,414 169,784 388,012 232,956 209,241 676,160 197,275 268,673 131,268 107,065 178,672 125,105 155,363 342,374 755,017 117,215 130,748

0.94 0.89 0.80 0.85 0.53 1.17 0.80 0.53 0.92 0.94 0.94 0.53 0.94 0.94 1.20 0.94 0.97 0.94 0.94 0.95 0.52 0.60 0.54 1.27 0.92 0.90 0.92 0.53

685,785 104,019 522,371 110,340 57,704 348,398 380,546 62,974 124,733 207,001 418,178 116,948 159,937 364,731 279,547 196,896 658,580 186,030 252,015 124,048 55,995 107,739 67,056 197,311 316,354 677,250 107,838 69,035

686 104 522 110 58 348 381 63 125 207 418 117 160 365 280 197 659 186 252 124 56 108 67 197 316 677 108 69

57

58


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Durango, Durango Ecatepec, México Ensenada, Baja California Fresnillo, Zacatecas General Escobedo, Nuevo León Gómez Palacio, Durango Guadalajara, Jalisco Guadalupe, Nuevo León Guadalupe, Zacatecas Guanajuato, Guanajuato Guasave, Sinaloa Guaymas, Sonora Hermosillo, Sonora Hidalgo del Parral, Chihuahua Hidalgo, Michoacán Huixquilucan, México Iguala, Guerrero Irapuato, Guanajuato Juárez, Chihuahua La Paz, Baja California Sur Lagos de Moreno, Jalisco Lázaro Cárdenas, Michoacán León, Guanajuato Lerdo, Durango Lerma, México Los Cabos, Baja California Sur Los Mochis-Topolobampo, Ahome, Sinaloa Madero, Tamaulipas Mante, Tamaulipas Manzanillo, Colima Matamoros, Tamaulipas Mazatlán, Sinaloa Mérida, Yucatán Metepec, México Mexicali, Baja California México, Federal District Minatitlán, Veracruz Monclova, Coahuila Monterrey, Nuevo León Morelia, Michoacán Naucalpan, México Navojoa, Sonora Nezahualcoyotl, México Nogales, Sonora Nuevo Laredo, Tamaulipas Oaxaca, Oaxaca Obregón, Cajeme, Sonora Orizaba, Veracruz Pachuca, Hidalgo Piedras Negras, Coahuila Poza Rica, Veracruz Puebla, Puebla Puerto Vallarta, Jalisco Querétaro, Querétaro Reynosa, Tamaulipas

Year 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

Urban Population 495,962 1,655,225 381,747 183,941 246,166 276,085 1,650,776 679,230 109,179 144,166 279,878 129,236 619,185 101,390 106,922 198,564 125,395 445,778 1,264,121 199,712 128,407 174,205 1,153,998 113,705 103,909 113,727 362,442 184,289 111,671 127,443 427,966 385,047 714,689 197,699 779,523 8,615,955 144,574 194,458 1,112,636 628,801 861,173 141,412 1,223,180 164,819 317,877 259,343 357,857 119,405 249,838 130,398 152,318 1,372,446 191,424 657,447 438,696

Generation Rate (kg/capita/day) 0.93 1.28 0.93 0.53 1.18 0.94 1.20 1.18 0.95 0.92 0.94 1.05 0.99 0.76 0.54 1.13 0.93 0.95 1.22 1.42 0.54 0.92 1.10 0.85 1.13 0.50 1.00 0.85 0.54 0.95 0.98 0.94 0.99 1.13 0.94 1.38 0.54 0.98 1.19 0.89 1.20 0.94 1.28 0.94 1.47 0.92 0.94 0.98 0.80 0.94 1.05 1.38 0.71 0.83 0.76

Total MSW Generated (kg/day) 461,245 2,118,688 355,025 98,041 289,245 258,139 1,980,931 801,491 103,174 132,921 263,925 135,698 615,470 76,752 57,310 224,377 116,994 423,489 1,543,492 283,591 68,955 160,965 1,269,398 96,649 117,417 56,864 362,442 155,908 59,967 121,071 419,407 361,944 705,398 223,400 733,531 11,890,018 78,070 190,569 1,324,037 556,489 1,033,408 132,927 1,565,670 154,930 467,279 237,818 336,386 117,256 198,621 122,574 159,934 1,893,975 135,528 542,394 333,409

Total Waste (tons/day) 461 2,119 355 98 289 258 1,981 801 103 133 264 136 615 77 57 224 117 423 1,543 284 69 161 1,269 97 117 57 362 156 60 121 419 362 705 223 734 11,890 78 191 1,324 556 1,033 133 1,566 155 467 238 336 117 199 123 160 1,894 136 542 333

ANNEX

ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Río Bravo, Tamaulipas Salamanca, Guanajuato Saltillo, Coahuila San Andrés Tuxtla, Veracruz San Cristobal de las Casas, Chiapas San Francisco del Rincón, Guanajuato San Juan Bautista de Tuxtepec, Oaxaca San Juan del Río, Querétaro San Luis Potosi, San Luis Potosi San Luis Río Colorado, Sonora San Martín Texmelucan, Puebla San Miguel de Allende, Guanajuato San Nicolas de los Garza, Nuevo León San Pedro Garza García , Nuevo León Santa Catarina, Nuevo León Silao, Guanajuato Soledad de Graciano, San Luis Potosi Tampico, Tamaulipas Tapachula, Chiapas Taxco, Guerrero Tecoman, Colima Tehuacán, Puebla Tepatitlán, Jalisco Tepic, Nayarit Tijuana, Baja California Tlajomulco, Jalisco Tlalnepantla, México Tlaquepaque, Jalisco Toluca, México Tonalá, Jalisco Torreón, Coahuila Tulancingo, Hidalgo Tuxpan, Veracruz Tuxtla Gutiérrez, Chiapas Uruapan, Michoacán Valle de Chalco Solidaridad, México Valle de Santiago, Guanajuato Valles, San Luis Potosi Veracruz, Veracruz Victoria, Tamaulipas Villahermosa, Centro, Tabasco Xalapa, Veracruz Zacatecas, Zacatecas Zamora, Michoacán Zapopan, Jalisco Zitacuaro, Michoacán Nicaragua Chinandega Leon Managua Masaya Tipitapa Panama

Year

Urban Population




2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

104,620 228,239 587,730 143,235 135,731 100,805 134,895 184,679 678,645 147,912 123,072 138,393 497,078 127,254 231,809 134,539 185,063 298,063 276,743 100,889 101,049 233,807 121,076 307,550 1,262,520 128,339 722,279 480,844 687,969 350,648 533,457 124,461 126,257 443,782 268,208 330,885 130,553 147,086 463,812 266,612 531,511 404,788 124,722 161,425 1,018,447 139,514

0.76 0.62 0.86 0.54 0.92 0.54 0.53 0.50 0.97 0.94 0.80 0.52 1.20 1.10 1.20 0.53 0.53 0.85 0.94 0.94 0.53 0.91 0.53 0.84 1.22 1.05 1.04 1.17 1.16 1.18 0.94 0.92 0.54 1.05 0.94 1.20 0.54 0.94 0.92 0.94 0.87 0.80 0.95 0.71 1.20 0.53

79,511 141,508 502,509 77,060 125,008 54,031 71,899 92,340 658,286 139,037 98,458 71,964 596,494 139,979 277,012 71,306 97,528 252,161 259,862 94,836 53,556 212,998 64,049 256,804 1,537,749 134,756 749,726 562,587 798,044 413,765 502,516 115,002 67,926 463,752 252,920 397,062 70,107 137,967 425,779 251,415 462,415 323,830 117,862 113,966 1,222,136 73,942

80 142 503 77 125 54 72 92 658 139 98 72 596 140 277 71 98 252 260 95 54 213 64 257 1,538 135 750 563 798 414 503 115 68 464 253 397 70 138 426 251 462 324 118 114 1,222 74

2001 2001 2001 2001 2001

124,107 147,845 952,068 115,369 108,861

0.61 0.62 0.71 0.61 0.43

75,085 90,925 676,920 70,029 46,266

75 91 677 70 46

59

60


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Arraiján Ciudad de Panamá Colón La Chorrera San Miguelito Paraguay Asunción Ciudad del Este Luque San Lorenzo Peru Callao, Callao Cercado Callao, Ventanilla Junín, El Tambo Junín, Huancayo Lima, Ate Lima, Carabayllo Lima, Chorrillos Lima, Comas Lima, El Agustino Lima, Independencia Lima, La Molina Lima, La Victoria Lima, Lima Cercado Lima, Los Olivos Lima, Lurigancho Lima, Puente Piedra Lima, Rímac Lima, San Borja Lima, San Juan de Lurigancho Lima, San Juan de Miraflores Lima, San Martín de Porres Lima, San Miguel Lima, Santa Anita Lima, Santiago de Surco Lima, Villa El Salvador Lima, Villa María del Triunfo Piura, Castilla Ucayali, Callería Saint Lucia St. Lucia Saint Vincent and the Grenadines* St. Vincent Suriname Greater Paramaribo Trinidad and Tobago Couva/Tabaquite/Talparo Diego Martin San Juan/Laventille Tunapuna/Piarco Uruguay

Year

Urban Population




2001 2001 2001 2001 2001

149,918 708,438 174,059 124,656 293,745

0.66 0.94 0.94 0.70 0.61

98,946 665,932 163,615 87,259 179,184

99 666 164 87 179

2001 2001 2001 2001

513,399 223,350 170,433 202,745

1.31 1.04 1.08 1.07

673,579 232,507 184,068 217,748

674 233 184 218

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

449,282 148,767 165,357 112,203 410,734 153,112 264,645 469,747 166,902 200,365 125,034 205,554 286,202 344,164 123,142 183,861 192,449 122,270 751,155 387,641 448,345 134,908 148,752 251,567 364,476 341,971 106,926 246,856

0.81 0.68 0.73 0.64 0.56 0.57 0.58 0.52 0.62 0.70 1.20 1.08 1.13 0.59 0.52 0.50 0.59 1.05 0.60 0.71 0.79 0.78 0.54 0.87 0.56 0.55 0.61 0.70

365,716 101,162 121,207 72,147 228,368 87,733 154,023 244,268 103,479 139,454 150,541 222,409 324,267 203,745 64,034 91,379 112,968 128,261 452,195 274,837 352,399 105,363 80,177 219,618 202,649 186,716 64,690 173,787

366 101 121 72 228 88 154 244 103 139 151 222 324 204 64 91 113 128 452 275 352 105 80 220 203 187 65 174

2001

162,157

1.18

191,345

191

2001

106,916

0.34

36,351

36

2001

287,131

1.00

287,131

287

2001 2001 2001 2001

162,779 105,720 157,295 203,975

0.70 0.70 3.20 2.20

113,945 74,004 503,344 448,745

114 74 503 449

ANNEX

ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 City Canelones Maldonado Montevideo Venezuela Distrito Capital Municipio Barinas Edo Barinas Municipio Caroni Edo Bolivar Municipio German Roscio Edo Guarico Municipio Girardot Edo Aragua Municipio Iribarren Edo Lara Municipio Lagunillas Edo Zulia Municipio Maracaibo Edo Zulia Municipio Pedraza Edo Apure Municipio Simon Rodriguez Edo Anzoategui Egypt (UNSD 2009) Cairo Iran (Damghani et al. 2008) Tehran Iraq (UNSD 2009) Baghdad India (CPCB 2005) Agartala Agra Ahmedabad Aizwal Allahabad Amritsar Asansol Banglore Bhopal Bhubaneswar Chandigarh Chennai Coimbatore Dehradun Delhi Dhanbad Faridabad Gandhinagar Greater Mumbai Guwahati Hyderabad Imphal Indore Jabalpur Jaipur Jammu Jamshedpur Kanpur Kochi

Year 2001 2001 2001

Urban Population


539,130 137,390 1,303,182

2001 1,836,286 2001 283,273 2001 704,168 2001 103,706 2001 396,125 2001 895,989 2001 144,345 2001 1,405,933 2001 283,273 2001 147,800 Middle East & North Africa



0.90 0.90 1.23

485,217 123,651 1,602,914

485 124 1,603

1.10 0.69 0.74 0.85 2.93 0.52 1.50 1.08 0.28 1.15

2,019,915 194,325 521,084 88,150 1,160,646 468,602 216,518 1,518,408 80,166 169,970

2,020 194 521 88 1,161 469 217 1,518 80 170

2007

7,765,000

1.77

13,766,234

13,766

2005

8,203,666

0.88

7,044,000

7,044

2005

6,784,000 South Asia

1.71

11,621,432

11,621

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

189,998 1,275,135 3,520,085 228,280 975,393 966,862 475,439 4,301,326 1,437,354 648,032 808,515 4,343,645 930,882 426,674 10,306,452 199,258 1,055,938 195,985 11,978,450 809,895 3,843,585 221,492 1,474,968 932,484 2,322,575 369,959 1,104,713 2,551,337 595,575

0.40 0.51 0.37 0.25 0.52 0.45 0.44 0.39 0.40 0.36 0.40 0.62 0.57 0.31 0.57 0.39 0.42 0.22 0.45 0.20 0.57 0.19 0.38 0.23 0.39 0.58 0.31 0.43 0.67

75,999 650,319 1,302,431 57,070 507,204 435,088 209,193 1,677,517 574,942 233,292 323,406 2,693,060 530,603 132,269 5,874,678 77,711 443,494 43,117 5,390,303 161,979 2,190,843 42,083 560,488 214,471 905,804 214,576 342,461 1,097,075 399,035

76 650 1,302 57 507 435 209 1,678 575 233 323 2,693 531 132 5,875 78 443 43 5,390 162 2,191 42 560 214 906 215 342 1,097 399

61

62


ANNEX F (continued)

MSW Generation Data for Cities Over 100,000 Urban Population




City

Year

Kolkata Lucknow Ludhiana Madurai Meerut Nagpur Nashik Patna Pondicherry Pune Raipur Rajkot Ranchi Shillong Simla Srinagar Surat Tiruvanantapuram Vadodara Varanasi Vijaywada Vishakhapatnam Nepal (Alam 2008) Kathmandu Sri Lanka (UNSD 2009) Dehiwala-Mount Lavinia Moratuwa

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

4,572,876 2,185,927 1,398,467 928,868 1,068,772 2,052,066 1,077,236 1,366,444 220,865 2,538,473 605,747 967,476 847,093 132,867 142,555 898,440 2,433,835 744,983 1,306,227 1,091,918 851,282 982,904

0.58 0.22 0.53 0.30 0.46 0.25 0.19 0.37 0.59 0.46 0.30 0.21 0.25 0.34 0.27 0.48 0.41 0.23 0.27 0.39 0.44 0.59

2,652,268 480,904 741,188 278,660 491,635 513,017 204,675 505,584 130,310 1,167,698 181,724 203,170 211,773 45,175 38,490 431,251 997,872 171,346 352,681 425,848 374,564 579,913

2,652 481 741 279 492 513 205 506 130 1,168 182 203 212 45 38 431 998 171 353 426 375 580

2003

738,173

0.31

226,800

227

2007 2007

209,787 189,790

0.73 0.67

154,110 127,854

154 128

NOTES: * Denotes domestic waste data as MSW figures are unknown. PAHO defines municipal waste and domestic waste as follows: PAHO definitions: Municipal waste Solid or semi-solid waste generated in of population centers including domestic and commercial wastes, as well as those originated by the, small-scale industries and institutions (including hospital and clinics); markets street sweeping, and from public cleansing. Domestic waste Domestic solid or semi-solid waste generated by human activities a the household level. ** China cities have populations over 750,000 inhabitants

ANNEX

ANNEX G

MSW Collection Data for Cities Over 100,000 City

Year

Urban Population

MSW Collection Coverage (%)

Africa Benin (UNSD 2009) Parakou

2002

148,450

10

1995

876,200

51

2005

1,720,000

43

2003

800,000

15 - 20

2002

2,777,000

30 - 40

2007

3,000,000

76

2006

2,312,000

30 - 45

N/A

611,883

20 - 30

2007

242,800

77

2003

1,708,000

30 - 40

N/A

2,500,000

48

2002

1,000,000

42

2005

1,300,000

18

2007

2,500,000

99

2007

6,926,000

100

2007

525,760

100

2004

8,962,000

83

2007

1,660,714

95

Burkina Faso (UNSD 2009) Ouagadougou Cameroon (Parrot et al. 2009) Yaounde Chad (Parrot et al. 2009) Ndjamena Côte d’Ivoire (Parrot et al. 2009) Abidjan Guinea (UNSD 2009) Conakry Kenya (Parrot et al. 2009) Nairobi Mauritania (Parrot et al. 2009) Nouakchott Niger (UNSD 2009) Zinder** Senegal (Parrot et al. 2009) Dakar Tanzania (Parrot et al. 2009) Dar es Salaam Togo (Parrot et al. 2009) Lome Zambia (UNSD 2009) Lusaka Zimbabwe (UNSD 2009) Harare

East Asia & Pacific (UNSD 2009) China, Hong Kong SAR Hong Kong China, Macao SAR Macao Indonesia Jakarta Philippines Manila

Eastern Europe & Central Asia (UNSD 2009) Albania Tirana Belarus Minsk Croatia Zagreb Georgia Tbilisi Batumi Kutaisi

2007

1,532,000

90

2007

1,806,200

100

2006

784,900

100

2007 2007 2007

1,300,000 303,200 185,960

100 62 95

63

64


ANNEX G (continued)


Year

Urban Population


Latin America and the Caribbean (PAHO, 2005) Argentina Area Metropolitana Buenos Aires

2001

12,544,018

Bahia Blanca

2001

285,000

100 100

Cordoba

2001

1,283,396

100

Neuquen

2001

202,518

100

Parana

2001

245,677

100

Rafaela

2001

100,000

100

Rio Cuarto

2001

154,127

100

Rosario

2001

906,004

100

Salta Capital

2001

472,971

100

2001

200,000

100

2001

268,792

100 90

Bahamas Nassau, Bahamas Barbados* Barbados Bolivia* Cochabamba

2001

717,026

El Alto

2001

629,955

76

La Paz

2001

790,353

87

Oruro

2001

201,230

92

Potosi

2001

135,783

88

Santa Cruz de la Sierra

2001

1,113,000

88

Sucre

2001

193,876

85

Tarija

2001

135,783

93

2001

318,779

99

Chile Antofagasta, Antofagasta Antofagasta, Calama

2001

138,402

100

Araucanía, Temuco

2001

245,347

100

B.O’Higgins, Rancagua

2001

214,344

100

Biobío, Chillán

2001

161,953

100

Biobío, Concepción

2001

216,061

100

Biobío, Talcahuano

2001

250,348

100

Coquimbo, Coquimbo

2001

163,036

100

Coquimbo, La Serena

2001

160,148

100

Los Lagos, Osorno

2001

145,475

100

Los Lagos, Puerto Montt

2001

175,938

100

Los Lagos, Valdivia

2001

140,559

100

Magallanes, Punta Arenas

2001

120,874

100

Maule, Curicó

2001

120,299

100

Maule, Talca

2001

203,231

100

Santiago, Cerro Navia

2001

148,312

100

Santiago, La Florida

2001

365,674

100

Santiago, La Pintana

2001

190,085

100

Santiago, Maipú

2001

468,390

100

Santiago, Providencia

2001

120,874

100

Santiago, Recoleta

2001

148,220

100

Santiago, Santiago

2001

200,792

100

Tarapacá, Arica

2001

185,268

100

Valparaíso, Valparaíso

2001

275,982

100

Valparaíso, Viña del Mar

2001

286,931

100

ANNEX

ANNEX G (continued)


Year

Urban Population


Colombia Armenia

2001

293,000

100

Barrancabermeja

2001

183,000

100 100

Barranquilla

2001

1,276,000

Bello

2001

353,000

97

Bogotá

2001

6,558,000

100 100

Bucaramanga

2001

543,000

Buenaventura

2001

230,000

80

Buga

2001

113,000

100

Cali

2001

2,181,000

97

Cartagena

2001

854,000

97

Cartago

2001

129,000

98

Cúcuta

2001

644,000

100 84

Dosquebradas

2001

166,000

Envigado

2001

145,000

99

Florencia

2001

116,000

80

Floridablanca

2001

232,000

95

Girardot

2001

117,000

95

Ibagué

2001

403,000

97

Itagüí

2001

246,000

98

Maicao

2001

115,000

100

Manizales

2001

345,000

100

Medellín

2001

1,909,000

100

Montería

2001

256,000

100

Neiva

2001

317,000

98

Palmira

2001

234,000

100 100

Pasto

2001

349,000

Pereira

2001

401,000

94

Popayán

2001

206,000

98

Santa Marta

2001

382,000

97

Sincelejo

2001

234,000

100

2001 2001 2001 2001 2001 2001 2001

285,000 114,000 310,000 157,000 112,000 278,000 289,000

95 81 100 100 100 98 98

2001 2001 2001

234,737 203,770 326,384

82 40 100

2001 2001 2001 2001 2001 2001 2001 2001

154,832 308,288 118,935 154,897 2,186,632 222,217 268,843 110,846

100 100 100 97 100 100 100 100

Soacha Sogamoso Soledad Tuluá Tunja Valledupar Villavicencio Costa Rica Alajuela Desamparados San José Cuba Bayamo Camagüey Ciego de Ávila Cienfuegos Ciudad de La Habana Guantánamo Holguín Manzanillo

65

66


ANNEX G (continued)

MSW Collection Data for Cities Over 100,000 City Matanzas Pinar del Río Sancti Spíritus Santa Clara Santiago de Cuba Tunas Dominican Republic La Romana Quito Santo Domingo de los Colorados Ecuador* Quito Santo Domingo de los Colorados El Salvador La Libertad - Nueva San Salvador San Miguel, San Miguel San Salvador - Apopa San Salvador - Ilopango, San Salvador - Mejicanos San Salvador - Soyapango San Salvador, San Salvador Santa Ana, Santa Ana Grenada Grenada Guatemala Antigua Guatemala Guatemala Quetzaltenango San Benito Guyana Georgetown Haiti Cap-Haïtien Carrefour Croix des Bouquets Delmas Gonaïves Jacmel Les Cayes Pétion Ville Port-au-Prince Saint Marc Honduras San Pedro Sula Jamaica* North Eastern Wasteshed( Portland, St.Mary and St.Ann) Retirement(Westmoreland,Hanover,Trelawny & St.James) Riverton ( Kgn, St.And, St.Cath. Clarendon and St.Thomas) Southern(Manchester, St. Elizabeth) Mexico Acapulco, Guerrero Acuña, Coahuila Aguascalientes, Aguascalientes Altamira, Tamaulipas Apatzingan, Michoacán Apodaca, Nuevo León


Year

Urban Population

2001 2001 2001 2001 2001 2001

133,177 162,078 109,220 220,345 452,307 144,381

100 100 91 98 100 100

2001 2001 2001

201,700 2,774,926 244,039

100 60 90

2001 2001

1,841,200 200,421

80 83

2001 2001 2001 2001 2001 2001 2001 2001

136,909 172,203 139,802 115,358 172,548 285,286 479,605 167,975

94 82 73 50 85 95 81 83

2001

95,551

100

2001 2001 2001 2001

248,019 2,541,581 122,157 366,735

80 70 90 80

2001

180,000

100

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

141,061 416,301 143,803 335,866 138,480 138,504 152,845 143,452 1,100,085 164,868

45 16 40 16 45 80 45 22 22 45

2001

483,384

85

2001 2001 2001 2001

357,265 452,724 1,458,155 331,190

56 68 66 48

2001 2001 2001 2001 2001 2001

728,010 117,271 656,245 130,425 108,466 297,776

85 85 90 85 85 100

ANNEX

ANNEX G (continued)

MSW Collection Data for Cities Over 100,000 City Atizapan de Zaragoza, México Atlixco, Puebla Boca del Río, Veracruz Campeche, Campeche Cancún, Benito Juárez, Quintana Roo Cárdenas, Tabasco Carmen, Campeche Celaya, Guanajuato Chalco, México Chetumal, Othon P. Blanco, Quintana Roo Chihuahua, Chihuahua Chilpancingo, Guerrero Coatzacoalcos, Veracruz Colima, Colima Comitán de Domínguez, Chiapas Córdoba, Veracruz Cuauhtemoc, Chihuahua Cuautla, Morelos Cuernavaca, Morelos Culiacán, Sinaloa Delicias, Chihuahua Dolores Hidalgo, Guanajuato Durango, Durango Ecatepec, México Ensenada, Baja California Fresnillo, Zacatecas General Escobedo, Nuevo León Gómez Palacio, Durango Guadalajara, Jalisco Guadalupe, Nuevo León Guadalupe, Zacatecas Guanajuato, Guanajuato Guasave, Sinaloa Guaymas, Sonora Hermosillo, Sonora Hidalgo del Parral, Chihuahua Hidalgo, Michoacán Huixquilucan, México Iguala, Guerrero Irapuato, Guanajuato Juárez, Chihuahua La Paz, Baja California Sur Lagos de Moreno, Jalisco Lázaro Cárdenas, Michoacán León, Guanajuato Lerdo, Durango Lerma, México Los Cabos, Baja California Sur Los Mochis-Topolobampo, Ahome, Sinaloa Madero, Tamaulipas Mante, Tamaulipas Manzanillo, Colima Matamoros, Tamaulipas Mazatlán, Sinaloa Mérida, Yucatán Metepec, México

Year

Urban Population

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

475,683 117,929 135,875 219,281 444,870 219,414 169,784 388,012 232,956 209,241 676,160 197,275 268,673 131,268 107,065 178,672 125,105 155,363 342,374 755,017 117,215 130,748 495,962 1,655,225 381,747 183,941 246,166 276,085 1,650,776 679,230 109,179 144,166 279,878 129,236 619,185 101,390 106,922 198,564 125,395 445,778 1,264,121 199,712 128,407 174,205 1,153,998 113,705 103,909 113,727 362,442 184,289 111,671 127,443 427,966 385,047 714,689 197,699

MSW Collection Coverage (%) 90 85 85 80 90 80 85 95 85 80 95 85 80 85 85 90 85 90 85 90 85 85 90 90 95 85 100 85 90 100 85 90 85 85 100 85 85 85 85 90 90 85 85 85 90 85 85 85 85 85 85 85 85 85 95 85

67

68


ANNEX G (continued)

MSW Collection Data for Cities Over 100,000 City Mexicali, Baja California México, Federal District Minatitlán, Veracruz Monclova, Coahuila Monterrey, Nuevo León Morelia, Michoacán Naucalpan, México Navojoa, Sonora Nezahualcoyotl, México Nogales, Sonora Nuevo Laredo, Tamaulipas Oaxaca, Oaxaca Obregón, Cajeme, Sonora Orizaba, Veracruz Pachuca, Hidalgo Piedras Negras, Coahuila Poza Rica, Veracruz Puebla, Puebla Puerto Vallarta, Jalisco Querétaro, Querétaro Reynosa, Tamaulipas Río Bravo, Tamaulipas Salamanca, Guanajuato Saltillo, Coahuila San Andrés Tuxtla, Veracruz San Cristobal de las Casas, Chiapas San Francisco del Rincón, Guanajuato San Juan Bautista de Tuxtepec, Oaxaca San Juan del Río, Querétaro San Luis Potosi, San Luis Potosi San Luis Río Colorado, Sonora San Martín Texmelucan, Puebla San Miguel de Allende, Guanajuato San Nicolas de los Garza, Nuevo León San Pedro Garza García , Nuevo León Santa Catarina, Nuevo León Silao, Guanajuato Soledad de Graciano, San Luis Potosi Tampico, Tamaulipas Tapachula, Chiapas Taxco, Guerrero Tecoman, Colima Tehuacán, Puebla Tepatitlán, Jalisco Tepic, Nayarit Tijuana, Baja California Tlajomulco, Jalisco Tlalnepantla, México Tlaquepaque, Jalisco Toluca, México Tonalá, Jalisco Torreón, Coahuila Tulancingo, Hidalgo Tuxpan, Veracruz

Year

Urban Population

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

779,523 8,615,955 144,574 194,458 1,112,636 628,801 861,173 141,412 1,223,180 164,819 317,877 259,343 357,857 119,405 249,838 130,398 152,318 1,372,446 191,424 657,447 438,696 104,620 228,239 587,730 143,235 135,731 100,805 134,895 184,679 678,645 147,912 123,072 138,393 497,078 127,254 231,809 134,539 185,063 298,063 276,743 100,889 101,049 233,807 121,076 307,550 1,262,520 128,339 722,279 480,844 687,969 350,648 533,457 124,461 126,257

MSW Collection Coverage (%) 80 100 85 85 100 85 90 85 80 85 100 80 85 90 95 100 85 95 85 100 85 85 90 90 85 85 90 85 90 85 90 85 90 100 100 100 90 85 85 85 85 85 90 85 80 95 85 95 95 85 95 100 85 85

ANNEX

ANNEX G (continued)

MSW Collection Data for Cities Over 100,000 MSW Collection Coverage (%)

City

Year

Urban Population

Tuxtla Gutiérrez, Chiapas Uruapan, Michoacán Valle de Chalco Solidaridad, México Valle de Santiago, Guanajuato Valles, San Luis Potosi Veracruz, Veracruz Victoria, Tamaulipas Villahermosa, Centro, Tabasco Xalapa, Veracruz Zacatecas, Zacatecas Zamora, Michoacán Zapopan, Jalisco Zitacuaro, Michoacán Nicaragua Chinandega Leon Managua Panama Arraiján Ciudad de Panamá Colón La Chorrera San Miguelito Paraguay Asunción Capiatá Ciudad del Este Fernando de la Mora Lambare Luque San Lorenzo Peru Callao, Callao Cercado Callao, Ventanilla Junín, El Tambo Junín, Huancayo Lima, Ate Lima, Carabayllo Lima, Chorrillos Lima, Comas Lima, El Agustino Lima, Independencia Lima, La Molina Lima, La Victoria Lima, Lima Cercado Lima, Los Olivos Lima, Lurigancho Lima, Puente Piedra Lima, Rímac Lima, San Borja Lima, San Juan de Lurigancho Lima, San Juan de Miraflores Lima, San Martín de Porres Lima, San Miguel

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

443,782 268,208 330,885 130,553 147,086 463,812 266,612 531,511 404,788 124,722 161,425 1,018,447 139,514

85 85 80 85 85 90 90 80 90 85 90 90 85

2001 2001 2001

124,107 147,845 952,068

80 70 80

2001 2001 2001 2001 2001

149,918 708,438 174,059 124,656 293,745

63 80 66 64 95

2001 2001 2001 2001 2001 2001 2001

513,399 154,469 223,350 114,332 119,984 170,433 202,745

99 35 60 97 42 54 26

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 2001

449,282 148,767 165,357 112,203 410,734 153,112 264,645 469,747 166,902 200,365 125,034 205,554 286,202 344,164 123,142 183,861 192,449 122,270 751,155 387,641 448,345 134,908

75 57 66 70 89 78 89 90 80 66 75 75 85 87 65 73 89 63 47 65 74 80

69

70


ANNEX G (continued)

MSW Collection Data for Cities Over 100,000 City Lima, Santa Anita Lima, Santiago de Surco Lima, Villa El Salvador Lima, Villa María del Triunfo Piura, Castilla Ucayali, Callería Saint Lucia St. Lucia Saint Vincent and the Grenadines* St. Vincent Suriname Greater Paramaribo Trinidad and Tobago Couva/Tabaquite/Talparo Diego Martin San Juan/Laventille Tunapuna/Piarco Uruguay Canelones Maldonado Montevideo Venezuela Distrito Capital Municipio Barinas Edo Barinas Municipio Caroni Edo Bolivar Municipio Girardot Edo Aragua Municipio Iribarren Edo Lara Municipio Lagunillas Edo Zulia Municipio Maracaibo Edo Zulia Municipio Pedraza Edo Apure Municipio Simon Bolivar Edo Anzoategui Municipio Simon Rodriguez Edo Anzoategui Egypt Cairo Iraq Baghdad Nepal (Alam 2008) Kathmandu Sri Lanka (UNSD 2009) Dehiwala-Mount Lavinia Moratuwa


Year

Urban Population

2001 2001 2001 2001 2001 2001

148,752 251,567 364,476 341,971 106,926 246,856

71 79 77 80 77 70

2001

162,157

100

2001

106,916

90

2001

287,131

82

2001 2001 2001 2001

162,779 105,720 157,295 203,975

100 100 100 100

2001 2001 2001

539,130 137,390 1,303,182

75 95 90

1,836,286 283,273 704,168 396,125 895,989 144,345 1,405,933 283,273 344,593 147,800

80 100 68 88 80 90 87 100 80 100

7,765,000

77

6,784,000

86

2003

738,173

94

2007 2007

209,787 189,790

96 90

2001 2001 2001 2001 2001 2001 2001 2001 2001 2001 Middle East & North Africa (UNSD 2009) 2007 2005 South Asia

NOTES: * Domestic waste data used as MSW figures not available; hence it is assumed that waste collection coverage is for domestic waste and not MSW ** Urban population data from 2007; Waste collection coverage data from 2006 PAHO definitions: Municipal waste Solid or semi-solid waste generated in of population centers including domestic and commercial wastes, as well as those originated by the, small-scale industries and institutions (including hospital and clinics); markets street sweeping, and from public cleansing. Domestic waste Domestic solid or semi-solid waste generated by human activities a the household level.

ANNEX

ANNEX H

MSW Disposal Methods for Cities Over 100,000

City

Urban Population

Sanitary Landfill (%)

Controlled Landfill (%)

Open Dump (%)

Watercourses (%)

Other (%)

Latin America & Caribbean (PAHO 2005) Argentina Area Metropolitana Buenos Aires Bahia Blanca Neuquen Parana Salta Capital Bolivia Cochabamba El Alto La Paz Oruro Potosi Santa Cruz de la Sierra Sucre Tarija Barbados Antofagasta, Antofagasta Antofagasta, Calama Araucanía, Temuco B.O'Higgins, Rancagua Barbados Biobío, Chillán Biobío, Concepción Biobío, Talcahuano Coquimbo, Coquimbo Coquimbo, La Serena Los Lagos, Osorno Los Lagos, Puerto Montt Los Lagos, Valdivia Magallanes, Punta Arenas Maule, Curicó Maule, Talca Santiago, Cerro Navia Santiago, La Florida Santiago, Maipú Santiago, Providencia Santiago, Recoleta Santiago, Santiago Tarapacá, Arica Valparaíso, Valparaíso Valparaíso, Viña del Mar Cuba Bayamo Camagüey Ciego de Ávila Cienfuegos Ciudad de La Habana Guantánamo Holguín Manzanillo

12,544,018 285,000 202,518 245,677 472,971

100 80 100 0 100

0 0 0 0 0

0 0 0 100 0

0 0 0 0 0

0 0 0 0 0

717,026 629,955 790,353 201,230 135,783 1,113,000 193,876 135,783

87 0 87 89 85 85 83 90

0 74 0 0 0 0 0 0

0 16 0 5 0 0 9 0

0 N.A. N.A. 0 0 9 0 0

13 11 13 7 15 6 9 10

318,779 138,402 245,347 214,344 268,792 161,953 216,061 250,348 163,036 160,148 145,475 175,938 140,559 120,874 120,299 203,231 148,312 365,674 468,390 120,874 148,220 200,792 185,268 275,982 286,931

0 0 98 100 35 0 0 0 0 0 100 0 83 0 100 100 100 100 99 100 100 86 0 100 0

100 75 0 0 48 0 100 75 100 100 0 96 0 85 0 0 0 0 0 0 0 0 95 0 99

0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 N.A. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 25 2 0 17 0 0 25 0 0 0 4 17 15 0 0 0 0 2 0 0 14 5 0 1

154,832 308,288 118,935 154,897 2,186,632 222,217 268,843 110,846

0 0 0 14 0 0 20 20

9 100 100 0 90 100 80 0

90 0 0 85 11 0 0 80

0 0 0 0 0 0 0 0

1 0 0 1 0 0 0 0

71

72


ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City Matanzas Pinar del Río Sancti Spíritus Santa Clara Santiago de Cuba Tunas Colombia Armenia Barrancabermeja Barranquilla Bello Bogotá Bucaramanga Buenaventura Buga Cali Cartagena Cartago Dosquebradas Envigado Florencia Floridablanca Ibagué Itagüí Maicao Manizales Medellín Montería Palmira Pasto Popayán Santa Marta Sincelejo Soacha Sogamoso Soledad Tuluá Valledupar Costa Rica Alajuela Cartago Desamparados Goicoechea Heredia Pérez Zeledón Pococí Puntarenas San Carlos San José Dominican Republic San Francisco de Macorís Santiago de los Caballeros Santo Domingo

Urban Population



Open Dump (%)

Watercourses (%)

Other (%)

133,177 162,078 109,220 220,345 452,307 144,381

0 20 0 93 100 81

100 80 88 0 0 0

0 0 13 5 0 19

0 0 0 0 0 0

0 0 0 2 0 0

293,000 183,000 1,276,000 353,000 6,558,000 543,000 230,000 113,000 2,181,000 854,000 129,000 166,000 145,000 116,000 232,000 403,000 246,000 115,000 345,000 1,909,000 256,000 234,000 349,000 206,000 382,000 234,000 285,000 114,000 310,000 157,000 278,000

100 0 100 97 100 0 0 100 0 100 82 100 99 0 0 99 98 0 100 100 0 100 99 0 0 100 0 100 0 100 95

0 0 0 0 0 98 0 0 0 0 0 0 0 0 90 0 0 0 0 0 0 0 0 98 86 0 0 0 0 0 0

0 100 0 0 0 0 0 0 100 0 0 0 0 100 0 0 0 0 0 0 100 0 0 0 0 0 100 0 100 0 0

0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 3 0 2 0 0 0 0 18 0 1 0 10 1 2 0 0 0 0 0 1 2 14 0 0 0 0 0 5

234,737 138,940 203,770 123,375 109,398 129,219 109,367 108,214 135,133 326,384

100 100 90 100 100 0 0 0 0 98

0 0 0 0 0 30 100 0 0 0

0 0 0 0 0 0 0 100 97 0

0 0 0 0 0 0 0 0 0 0

0 0 10 0 0 70 0 0 3 2

210,580 594,424 2,774,926

0 0 83

0 0 10

100 100 0

0 0 3

0 0 4

ANNEX

ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City Ecuador Quito Santo Domingo de los Colrados El Salvador San Salvador, San Salvador San Salvador - Soyapango Grenada Grenada Guatemala Guatemala Guyana Georgetown Haiti Cap-Haïtien Carrefour Croix des Bouquets Delmas Gonaïves Jacmel Les Cayes Pétion Ville Port-au-Prince Saint Marc Honduras Distrito Central Jamaica North Eastern Wasteshed( Portland, St.Mary and St.Ann) Portmore Retirement(Westmoreland,Hanover,Trelawny & St.James) Riverton ( Kgn, St.And, St.Cath. Clarendon and St.Thomas) Southern(Manchester, St.Elizabeth) Southern(Manchester, St.Elizabeth) Mexico Acapulco, Guerrero Acuña, Coahuila Aguascalientes, Aguascalientes Altamira, Tamaulipas Apatzingan, Michoacán Apodaca, Nuevo León Atizapan de Zaragoza, México Atlixco, Puebla Boca del Río, Veracruz Campeche, Campeche Cancún, Benito Juárez, Quintana Roo Cárdenas, Tabasco Carmen, Campeche Celaya, Guanajuato Chalco, México Chetumal, Othon P. Blanco, Quintana Roo Chihuahua, Chihuahua Chilpancingo, Guerrero Coatzacoalcos, Veracruz Colima, Colima

Urban Population



Open Dump (%)

Watercourses (%)

Other (%)

1,841,200 200,421

84 0

0 91

0 0

0 0

16 9

479,605 285,286

81 95

0 0

0 0

0 0

19 5

95,551

90

0

0

0

10

2,541,581

0

40

0

0

60

180,000

0

90

0

10

0

141,061 416,301 143,803 335,866 138,480 138,504 152,845 143,452 1,100,085 164,868

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

65 38 80 44 60 35 54 38 30 54

25 0 0 0 0 0 23 26 0 23

10 62 20 56 40 65 23 36 70 23

819,867

0

100

0

0

0

357,265 159,974 452,724 1,458,155 331,190 331,190

0 0 0 0 0 0

100 100 100 100 100 100

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

728,010 117,271 656,245 130,425 108,466 297,776 475,683 117,929 135,875 219,281 444,870 219,414 169,784 388,012 232,956 209,241 676,160 197,275 268,673 131,268

94 0 94 0 0 93 94 0 0 0 94 0 0 94 0 0 93 0 0 94

0 0 0 94 0 0 0 0 94 0 0 0 0 0 0 0 0 0 0 0

0 94 0 0 94 0 0 94 0 94 0 94 94 0 94 94 0 94 94 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 6 6 6 6 7 6 6 6 6 6 6 6 6 6 6 7 6 6 6

73

74


ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City Comitán de Domínguez, Chiapas Córdoba, Veracruz Cuauhtemoc, Chihuahua Cuautla, Morelos Cuernavaca, Morelos Culiacán, Sinaloa Delicias, Chihuahua Dolores Hidalgo, Guanajuato Durango, Durango Ecatepec, México Ensenada, Baja California Fresnillo, Zacatecas General Escobedo, Nuevo León Gómez Palacio, Durango Guadalajara, Jalisco Guadalupe, Nuevo León Guadalupe, Zacatecas Guanajuato, Guanajuato Guasave, Sinaloa Guaymas, Sonora Hermosillo, Sonora Hidalgo del Parral, Chihuahua Hidalgo, Michoacán Huixquilucan, México Iguala, Guerrero Irapuato, Guanajuato Juárez, Chihuahua La Paz, Baja California Sur Lagos de Moreno, Jalisco Lázaro Cárdenas, Michoacán León, Guanajuato Lerdo, Durango Lerma, México Los Cabos, Baja California Sur Los Mochis-Topolobampo, Ahome, Sinaloa Madero, Tamaulipas Mante, Tamaulipas Manzanillo, Colima Matamoros, Tamaulipas Mazatlán, Sinaloa Mérida, Yucatán Metepec, México Mexicali, Baja California México, Distrito Federal Minatitlán, Veracruz Monclova, Coahuila Monterrey, Nuevo León Morelia, Michoacán Naucalpan, México Navojoa, Sonora Nezahualcoyotl, México Nogales, Sonora

Urban Population 107,065 178,672 125,105 155,363 342,374 755,017 117,215 130,748 495,962 1,655,225 381,747 183,941 246,166 276,085 1,650,776 679,230 109,179 144,166 279,878 129,236 619,185 101,390 106,922 198,564 125,395 445,778 1,264,121 199,712 128,407 174,205 1,153,998 113,705 103,909 113,727 362,442 184,289 111,671 127,443 427,966 385,047 714,689 197,699 779,523 8,615,955 144,574 194,458 1,112,636 628,801 861,173 141,412 1,223,180 164,819

Sanitary Landfill (%) 0 0 0 94 0 94 0 0 92 94 0 0 93 0 0 93 0 94 94 0 94 0 0 0 0 0 92 0 0 0 92 0 0 94 94 0 0 0 94 0 93 0 0 92 0 0 93 0 0 0 0 94

Controlled Landfill (%) 0 0 0 0 0 0 0 0 0 0 0 94 0 92 94 0 0 0 0 0 0 0 0 94 0 94 0 0 0 94 0 0 0 0 0 0 0 0 0 94 0 94 94 0 0 0 0 0 94 0 70 0

Open Dump (%) 94 94 94 0 94 0 94 94 0 0 94 0 0 0 0 0 94 0 0 94 0 94 94 0 92 0 0 92 94 0 0 94 94 0 0 94 94 94 0 0 0 0 0 0 94 94 0 94 0 94 23 0

Watercourses (%)

Other (%)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 6 6 6 6 6 6 6 8 6 6 6 7 8 6 7 6 6 6 6 6 6 6 6 8 6 8 8 6 6 8 6 6 6 6 6 6 6 6 6 7 6 6 8 6 6 7 6 6 6 7 6

ANNEX

ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City Nuevo Laredo, Tamaulipas Oaxaca, Oaxaca Obregón, Cajeme, Sonora Orizaba, Veracruz Pachuca, Hidalgo Piedras Negras, Coahuila Poza Rica, Veracruz Puebla, Puebla Puerto Vallarta, Jalisco Querétaro, Querétaro Reynosa, Tamaulipas Río Bravo, Tamaulipas Salamanca, Guanajuato Saltillo, Coahuila San Andrés Tuxtla, Veracruz San Cristobal de las Casas, Chiapas San Francisco del Rincón, Guanajuato San Juan Bautista de Tuxtepec, Oaxaca San Juan del Río, Querétaro San Luis Potosi, San Luis Potosi San Luis Río Colorado, Sonora San Martín Texmelucan, Puebla San Miguel de Allende, Guanajuato San Nicolas de los Garza, Nuevo León San Pedro Garza García , Nuevo León Santa Catarina, Nuevo León Silao, Guanajuato Soledad de Graciano, San Luis Potosi Tampico, Tamaulipas Tapachula, Chiapas Taxco, Guerrero Tecoman, Colima Tehuacán, Puebla Tepatitlán, Jalisco Tepic, Nayarit Tijuana, Baja California Tlajomulco, Jalisco Tlalnepantla, México Tlaquepaque, Jalisco Toluca, México Tonalá, Jalisco Torreón, Coahuila Tulancingo, Hidalgo Tuxpan, Veracruz Tuxtla Gutiérrez, Chiapas Uruapan, Michoacán Valle de Chalco Solidaridad, México Valle de Santiago, Guanajuato Valles, San Luis Potosi Veracruz, Veracruz Victoria, Tamaulipas Villahermosa, Centro, Tabasco

Urban Population 317,877 259,343 357,857 119,405 249,838 130,398 152,318 1,372,446 191,424 657,447 438,696 104,620 228,239 587,730 143,235 135,731 100,805 134,895 184,679 678,645 147,912 123,072 138,393 497,078 127,254 231,809 134,539 185,063 298,063 276,743 100,889 101,049 233,807 121,076 307,550 1,262,520 128,339 722,279 480,844 687,969 350,648 533,457 124,461 126,257 443,782 268,208 330,885 130,553 147,086 463,812 266,612 531,511

Sanitary Landfill (%) 96 0 0 94 94 94 94 93 94 94 0 94 0 94 0 0 0 0 94 94 0 0 94 93 93 93 94 0 0 94 0 0 0 0 94 94 92 94 0 0 0 94 0 94 0 0 0 0 0 0 94 0

Controlled Landfill (%) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 94 94 0 0 0 0 94 94 94 0 0 0 0 0 0 0 0 94 0 0

Open Dump (%) 0 94 94 0 0 0 0 0 0 0 94 0 94 0 94 94 92 94 0 0 94 94 0 0 0 0 0 94 94 0 94 94 0 0 0 0 0 0 0 0 0 0 94 0 94 94 94 94 94 0 0 94

Watercourses (%)

Other (%)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 6 6 6 6 6 6 7 6 6 6 6 6 6 6 6 8 6 6 6 6 6 6 7 7 7 6 6 6 6 6 6 6 6 6 6 8 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

75

76


ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City Xalapa, Veracruz Zacatecas, Zacatecas Zamora, Michoacán Zapopan, Jalisco Zitacuaro, Michoacán Nicaragua Chinandega Managua Masaya Tipitapa Panama Arraiján Ciudad de Panamá Colón La Chorrera San Miguelito Paraguay Asunción Luque Peru Callao, Callao Cercado Callao, Ventanilla Junín, El Tambo Junín, Huancayo Lima, Ate Lima, Carabayllo Lima, Chorrillos Lima, Comas Lima, El Agustino Lima, Independencia Lima, La Molina Lima, La Victoria Lima, Lima Cercado Lima, Los Olivos Lima, Lurigancho Lima, Puente Piedra Lima, Rímac Lima, San Borja Lima, San Juan de Lurigancho Lima, San Juan de Miraflores Lima, San Martín de Porres Lima, San Miguel Lima, Santa Anita Lima, Santiago de Surco Lima, Villa El Salvador Lima, Villa María del Triunfo Piura, Castilla Ucayali, Callería

Urban Population



Open Dump (%)

Watercourses (%)

Other (%)

404,788 124,722 161,425 1,018,447 139,514

0 0 0 92 0

0 0 0 0 0

94 94 94 0 94

0 0 0 0 0

6 6 6 8 6

124,107 952,068 115,369 108,861

0 0 0 0

0 49 0 0

58 0 71 61

0 0 0 0

42 51 29 39

149,918 708,438 174,059 124,656 293,745

0 80 0 0 95

0 0 0 0 0

63 N.D. 66 64 N.D.

N.D. N.D. N.D. N.D. N.D.

37 20 34 36 5

513,399 170,433

37 0

61 100

0 0

0 0

2 0

449,282 148,767 165,357 112,203 410,734 153,112 264,645 469,747 166,902 200,365 125,034 205,554 286,202 344,164 123,142 183,861 192,449 122,270 751,155 387,641 448,345 134,908 148,752 251,567 364,476 341,971 106,926 246,856

0 0 0 0 0 70 0 80 0 0 0 0 76 78 0 0 0 0 0 0 66 0 0 70 0 0 0 0

67 51 59 63 79 0 79 0 71 59 67 66 0 0 58 65 79 56 42 58 0 71 63 0 68 71 69 62

18 35 26 21 3 14 3 2 12 28 20 21 11 5 27 19 3 32 46 29 20 13 21 15 16 12 16 23

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

15 14 15 16 18 16 18 18 17 13 13 13 13 17 15 16 18 12 12 13 14 16 16 15 16 17 15 15

ANNEX

ANNEX H (continued)

MSW Disposal Methods for Cities Over 100,000 City St. Lucia St. Lucia St. Vincent and the Grenadines St. Vincent Suriname Greater Paramaribo Trinidad and Tobago Couva/Tabaquite/Talparo Diego Martin San Juan/Laventille Tunapuna/Piarco Uruguay Canelones Maldonado Montevideo Venezuela Municipio Guacara Carabobo Municipio Valencia Edo Carabobo

Urban Population



Open Dump (%)

Watercourses (%)

Other (%)

162,157

70

18

0

0

13

106,916

80

0

0

0

20

287,131

0

0

100

0

0

162,779 105,720 157,295 203,975

0 0 0 0

100 100 100 100

0 0 0 0

0 0 0 0

0 0 0 0

539,130 137,390 1,303,182

0 100 0

0 0 100

100 0 0

0 0 0

0 0 0

142,227 742,145

0 0

0 100

100 0

0 0

0 0

77

78


ANNEX I

MSW Composition Data for Cities Over 100,000 Region/Country/ City

Year

Urban Population

Organic (%)

Total Recyclables (%)

Paper (%)

Plastic (%)

Glass (%)

Metal (%)

Other (%)

4

—

1

28

13

5

1

12

Africa Ghana (Asase 2009) Kumasi

2008

1,610,867

Cambodia (Kum et al. 2005) Phnom Penh 2002 Egypt Cairo Jordan Amman Saudi Arabia Riyadh Syria Aleppo Tunisia Tunis Yemen Aden India (CPCB 2005) Agartala Agra Ahmedabad Aizwal Allahabad Amritsar Asansol Bangalore Bhopal Bhubaneswar Chandigarh Chennai Coimbatore Daman Dehradun Delhi Dhanbad Faridabad Gandhinagar Gangtok Greater Mumbai Guwahati Hyderabad Imphal Indore Itanagar Jabalpur Jaipur Jammu Jamshedpur

64

— East Asia & Pacific

3

65 — 4 Middle East & North Africa (Al-Yousfi)

2002

67

—

18

3

3

2

7

2002

55

—

14

13

3

2

13

2002

34

—

31

2

3

16

14

2002

59

—

13

12

8

1

8

2002

68

—

10

11

3

2

6

2002

57

11

11

3

5

14

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

28 38 48 25 45 21 35 26 25 38 32 42 34 48 29 30 37 35 53 37 21 23 24 21 38 27 25 42 27 41

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

1,89,998 12,75,135 35,20,085 2,28,280 9,75,393 9,66,862 4,75,439 43,01,326 14,37,354 6,48,032 8,08,515 43,43,645 9,30,882 35,770 4,26,674 1,03,06,452 1,99,258 10,55,938 1,95,985 29,354 1,19,78,450 8,09,895 38,43,585 2,21,492 14,74,968 35,022 9,32,484 23,22,575 3,69,959 11,04,713

59 46 41 54 35 65 50 52 52 50 57 41 50 30 51 54 47 42 34 47 62 54 54 60 49 52 58 46 52 43

— South Asia 14 16 12 21 19 14 14 22 22 13 11 16 16 22 20 16 16 23 13 16 17 23 22 19 13 21 17 12 21 16

ANNEX

ANNEX I (continued)

MSW Composition Data for Cities Over 100,000 Region/Country/ City

Year

Kanpur 2005 Kavarati 2005 Kochi 2005 Kohima 2005 Kolkata 2005 Lucknow 2005 Ludhiana 2005 Madurai 2005 Meerut 2005 Nagpur 2005 Nasik 2005 Panjim 2005 Patna 2005 Pondicherry 2005 Port Blair 2005 Pune 2005 Raipur 2005 Rajkot 2005 Ranchi 2005 Shillong 2005 Silvassa 2005 Simla 2005 Srinagar 2005 Surat 2005 Tiruvananthapuram 2005 Vadodara 2005 Varanasi 2005 Vijaywada 2005 Visakhapatnam 2005 Nepal (calculated from Alam 2008) Kathmandu

Urban Population

Organic (%)

Total Recyclables (%)

Paper (%)

Plastic (%)

Glass (%)

Metal (%)

Other (%)

25,51,337 10,119 5,95,575 77,030 45,72,876 21,85,927 13,98,467 9,28,868 10,68,772 20,52,066 10,77,236 59,066 13,66,444 2,20,865 99,984 25,38,473 6,05,747 9,67,476 8,47,093 1,32,867 50,463 1,42,555 8,98,440 24,33,835 7,44,983 13,06,227 10,91,918 8,51,282 9.82,904

48 46 57 57 51 47 50 55 55 47 40 62 52 50 48 62 51 42 51 63 72 43 62 57 73 47 45 59 46

12 27 19 23 11 16 19 17 11 16 25 17 13 24 28 17 16 11 10 17 14 37 18 11 14 15 17 17 24

— — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — — — — — — — — — — —

41 27 23 20 38 37 31 27 35 37 35 21 35 26 24 21 32 47 39 20 14 20 20 32 13 38 38 23 30

738,173

68

—

8

—

2

11

11

79

80


ANNEX J

MSW Generation by Country — Current Data and Projections for 2025

Country

Albania Algeria Angola Antigua and Barbuda Argentina Armenia Australia Austria Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo, Dem. Rep. Congo, Rep. Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Eritrea Estonia Ethiopia Fiji

Income Level LMI LMI LMI HIC UMI LMI HIC HIC HIC HIC LI HIC UMI HIC UMI LI LMI LMI UMI UMI HIC UMI LI LI LMI HIC LMI LI LI UMI LMI LMI LI LI LMI UMI LI UMI UMI HIC HIC HIC UMI LMI LMI LMI LMI LI HIC LI UMI

Region

ECA MENA AFR LCR LCR ECA OECD OECD LCR MENA SAR LCR ECA OECD LCR AFR SAR LCR AFR LCR EAP ECA AFR AFR AFR OECD AFR AFR AFR LCR EAP LCR AFR AFR AFR LCR AFR ECA LCR ECA OECD OECD LCR LCR LCR MENA LCR AFR ECA AFR EAP

Current Available Data MSW GenTotal MSW Generation Total Urban eration Per Population Capita (kg/ (tonnes/ capita/day) day) 1,418,524 19,225,335 8,973,498 24,907 33,681,145 1,964,525 16,233,664 5,526,033 252,689 574,671 38,103,596 92,289 7,057,977 10,265,273 124,224 3,147,050 225,257 5,587,410 860,779 144,507,175 282,415 5,423,113 2,549,805 700,922 7,914,528 21,287,906 274,049 1,596,934 2,566,839 13,450,282 511,722,970 29,283,628 161,070 18,855,716 2,056,826 2,390,195 9,006,597 2,539,903 8,447,447 595,707 7,547,813 4,684,754 50,793 5,625,356 7,599,288 29,894,036 3,504,687 878,184 931,657 12,566,942 339,328

0.77 1.21 0.48 5.50 1.22 0.68 2.23 2.40 3.25 1.10 0.43 4.75 0.78 1.33 2.87 0.54 1.46 0.33 1.03 1.03 0.87 1.28 0.51 0.55 0.77 2.33 0.50 0.50 0.50 1.08 1.02 0.95 2.23 0.50 0.53 1.36 0.48 0.29 0.81 2.07 1.10 2.34 1.24 1.18 1.13 1.37 1.13 0.50 1.47 0.30 2.10

1,088 23,288 4,329 137 41,096 1,342 36,164 13,288 822 630 16,384 438 5,479 13,690 356 1,699 329 1,863 890 149,096 247 6,959 1,288 384 6,082 49,616 137 795 1,288 14,493 520,548 27,918 359 9,425 1,096 3,260 4,356 740 6,822 1,230 8,326 10,959 63 6,658 8,603 40,822 3,945 438 1,367 3,781 712

Total Population 3,488,000 42,882,000 27,324,000 101,000 46,115,000 2,908,000 24,393,000 8,622,000 397,000 972,000 206,024,000 303,000 8,668,000 10,742,000 389,000 14,460,000 819,000 12,368,000 2,265,000 228,833,000 526,000 6,551,000 23,729,000 15,040,000 25,136,000 37,912,000 750,000 5,831,000 17,504,000 19,266,000 1,445,782,000 55,563,000 1,217,000 107,481,000 5,362,000 5,549,000 26,233,000 4,274,000 11,231,000 1,018,000 9,910,000 5,578,000 69,000 12,172,000 16,074,000 98,513,000 8,525,000 7,684,000 1,252,000 124,996,000 905,000

2025 MSW GenUrban eration Per Population Capita (kg/ capita/day) 2,006,000 31,778,000 18,862,000 35,000 43,470,000 1,947,000 22,266,000 6,204,000 346,000 875,000 76,957,000 152,000 6,903,000 10,511,000 237,000 7,286,000 428,000 9,047,000 1,591,000 206,850,000 426,000 5,011,000 6,899,000 2,577,000 17,194,000 31,445,000 526,000 2,634,000 6,566,000 17,662,000 822,209,000 44,179,000 405,000 48,980,000 3,678,000 3,973,000 15,677,000 2,735,000 8,763,000 760,000 7,575,000 5,027,000 55,000 9,523,000 12,027,000 46,435,000 5,726,000 2,368,000 903,000 30,293,000 557,000

1.2 1.45 0.7 4.3 1.85 1.2 2.1 2.15 2.9 1.6 0.75 4 1.2 1.8 2.3 0.75 1.7 0.7 1.4 1.6 1.3 1.6 0.75 0.8 1 2.2 0.7 0.7 0.7 1.5 1.7 1.5 2.1 0.75 0.75 1.8 0.7 0.8 1.3 2.1 1.65 2.15 1.6 1.5 1.5 1.8 1.6 0.7 1.7 0.65 2.1

Total MSW Generation (tonnes/day) 2,407 46,078 13,203 151 80,420 2,336 46,759 13,339 1,003 1,400 57,718 608 8,284 18,920 545 5,465 728 6,333 2,227 330,960 554 8,018 5,174 2,062 17,194 69,179 368 1,844 4,596 26,493 1,397,755 66,269 851 36,735 2,759 7,151 10,974 2,188 11,392 1,596 12,499 10,808 88 14,285 18,041 83,583 9,162 1,658 1,535 19,690 1,170

ANNEX

81

ANNEX J (continued)


Country

Income Level

Region

Current Available Data MSW GenTotal MSW Generation Total Urban eration Per Population Capita (kg/ (tonnes/ capita/day) day)

Total Population

2025 MSW GenUrban eration Per Population Capita (kg/ capita/day)

Total MSW Generation (tonnes/day)

Finland France

HIC HIC

OECD OECD

3,301,950 47,192,398

2.13 1.92

7,030 90,493

5,464,000 65,769,000

3,805,000 53,659,000

2.1 2

7,991 107,318

Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guyana Haiti Honduras Hong Kong, China Hungary Iceland India Indonesia Iran, Islamic Rep. Ireland Israel Italy Jamaica Japan Jordan Kenya Korea, South Kuwait Lao PDR Latvia Lebanon Lesotho Lithuania Luxembourg Macao, China Macedonia, FYR Madagascar Malawi Malaysia Maldives Mali Malta Mauritania Mauritius Mexico Mongolia Morocco Mozambique Myanmar Namibia Nepal

UMI LI LMI HIC LI HIC UMI LMI LMI LI LMI HIC HIC HIC LMI LMI LMI HIC HIC HIC UMI HIC LMI LI HIC HIC LI UMI UMI LMI UMI HIC HIC LMI LI LI UMI LMI LI HIC LI UMI UMI LMI LMI LI LI LMI LI

AFR AFR ECA OECD AFR OECD LCR LCR LCR LCR LCR EAP OECD OECD SAR EAP MENA OECD MENA OECD LCR OECD MENA AFR OECD MENA EAP ECA MENA AFR ECA OECD EAP ECA AFR AFR EAP SAR AFR MENA AFR AFR LCR EAP MENA AFR EAP AFR SAR

1,144,675 822,588 2,316,296 60,530,216 11,680,134 6,755,967 31,324 5,237,139 215,946 3,227,249 2,832,769 6,977,700 6,717,604 280,148 321,623,271 117,456,698 46,219,250 2,589,698 5,179,120 39,938,760 1,353,969 84,330,180 3,850,403 6,615,510 38,895,504 2,683,301 1,916,209 1,549,569 3,244,163 461,534 2,256,263 390,776 466,162 1,341,972 4,653,890 2,288,114 14,429,641 70,816 3,900,064 384,809 1,197,094 519,206 79,833,562 1,370,974 15,753,989 7,706,816 12,847,522 708,907 3,464,234

0.45 0.53 1.69 2.11 0.09 2.00 2.71 2.00 5.33 1.00 1.45 1.99 1.92 1.56 0.34 0.52 0.16 3.58 2.12 2.23 0.18 1.71 1.04 0.30 1.24 5.72 0.70 1.03 1.18 0.50 1.10 2.31 1.47 1.06 0.80 0.50 1.52 2.48 0.65 1.78 0.50 2.30 1.24 0.66 1.46 0.14 0.44 0.50 0.12

521 438 3,904 127,816 1,000 13,499 85 10,466 1,151 3,233 4,110 13,890 12,904 438 109,589 61,644 7,197 9,260 10,959 89,096 247 144,466 4,000 2,000 48,397 15,342 1,342 1,600 3,836 230 2,474 904 685 1,425 3,734 1,151 21,918 175 2,534 685 603 1,195 99,014 904 23,014 1,052 5,616 356 427

1,698,000 2,534,000 3,945,000 80,341,000 31,993,000 11,236,000 108,000 19,926,000 683,000 12,305,000 9,682,000 8,305,000 9,448,000 337,000 1,447,499,000 271,227,000 88,027,000 5,275,000 8,722,000 58,079,000 2,908,000 121,614,000 8,029,000 57,176,000 49,019,000 3,988,000 7,713,000 2,072,000 4,784,000 2,211,000 3,102,000 569,000 535,000 2,001,000 29,954,000 21,353,000 33,769,000 411,000 20,589,000 431,000 4,548,000 1,406,000 124,695,000 3,112,000 37,865,000 28,954,000 55,374,000 2,560,000 38,855,000

1,524,000 1,726,000 2,272,000 61,772,000 19,713,000 7,527,000 40,000 11,478,000 230,000 7,966,000 5,544,000 8,305,000 7,011,000 314,000 538,055,000 178,731,000 66,930,000 3,564,000 8,077,000 42,205,000 1,733,000 86,460,000 6,486,000 16,952,000 41,783,000 3,934,000 3,776,000 1,476,000 4,275,000 850,000 2,193,000 473,000 535,000 1,493,000 11,350,000 6,158,000 27,187,000 233,000 8,987,000 416,000 2,203,000 674,000 102,258 1,965,000 23,994,000 14,493,000 24,720,000 1,226,000 10,550,000

0.7 0.75 1.85 2.05 0.5 2 2.3 2 3.5 1.4 1.8 2 2 1.7 0.7 0.85 0.6 3 2.1 2.05 0.9 1.7 1.3 0.6 1.4 4 1.1 1.45 1.7 0.8 1.5 2.2 1.75 1.6 1.1 0.8 1.9 2.2 0.95 2 0.8 2.2 1.75 0.95 1.85 0.5 0.85 0.9 0.7

1,067 1,295 4,203 126,633 9,857 15,054 92 22,956 805 11,152 9,979 16,610 14,022 534 376,639 151,921 40,158 10,692 16,962 86,520 1,560 146,982 8,432 10,171 58,496 15,736 4,154 2,140 7,268 680 3,290 1,041 936 2,389 12,485 4,926 51,655 513 8,538 832 1,762 1,483 179 1,867 44,389 7,247 21,012 1,103 7,385

82


ANNEX J (continued)


Country

Netherlands New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation Rwanda Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia Solomon Islands South Africa Spain Sri Lanka St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan

Income Level

Region

Current Available Data MSW GenTotal MSW Generation Total Urban eration Per Population Capita (kg/ (tonnes/ capita/day) day)

Total Population


Total MSW Generation (tonnes/day)

HIC HIC LMI LI LI HIC HIC LI UMI LMI LMI LMI UMI HIC HIC UMI UMI LI LI HIC LI UMI UMI LI HIC HIC HIC LI UMI HIC LMI UMI UMI UMI

OECD OECD LCR AFR AFR OECD MENA SAR LCR LCR LCR EAP ECA OECD MENA ECA ECA AFR AFR MENA AFR ECA AFR AFR EAP OECD ECA EAP AFR OECD SAR LCR LCR LCR

13,197,842 3,612,147 2,848,165 2,162,063 73,178,110 3,605,500 1,629,404 60,038,941 2,008,299 3,052,320 18,678,510 58,654,205 23,398,400 6,162,205 759,577 11,648,240 107,386,402 1,573,625 88,673 15,388,239 4,693,019 3,830,299 43,172 2,029,398 4,839,400 3,036,442 986,862 50,992 26,720,493 33,899,073 2,953,410 15,069 44,119 48,255

2.12 3.68 1.10 0.49 0.56 2.80 0.70 0.84 1.21 0.21 1.00 0.50 0.88 2.21 1.33 1.04 0.93 0.52 0.49 1.30 0.52 0.79 2.98 0.45 1.49 1.37 1.21 4.30 2.00 2.13 5.10 5.45 4.35 1.70

27,945 13,293 3,123 1,068 40,959 10,082 1,142 50,438 2,438 630 18,740 29,315 20,630 13,616 1,014 12,082 100,027 822 44 20,000 2,438 3,041 129 904 7,205 4,164 1,192 219 53,425 72,137 15,068 82 192 82

16,960,000 4,764,000 7,075,000 26,250,000 210,129,000 5,228,000 3,614,000 224,956,000 4,267,000 8,026,000 34,148,000 115,878,000 36,337,000 10,712,000 1,102,000 19,494,000 128,193,000 15,220,000 216,000 34,797,000 17,999,000 9,959,000 94,000 8,639,000 5,104,000 5,308,000 1,941,000 705,000 52,300,000 46,623,000 20,328,000 61,000 195,000 125,000

14,860,000 4,229,000 4,478,000 5,503,000 126,634,000 4,187,000 2,700,000 104,042,000 3,501,000 5,584,000 25,593,000 86,418,000 23,236,000 7,389,000 1,066,000 11,783,000 96,061,000 3,831,000 155,000 29,661,000 8,992,000 5,814,000 60,000 3,949,000 5,104,000 3,300,000 958,000 183,000 36,073,000 37,584,000 3,830,000 23,000 64,000 69,000

2.1 3 1.5 0.75 0.8 2.3 1.15 1.05 1.65 0.6 1.4 0.9 1.2 2.15 1.7 1.45 1.25 0.85 0.9 1.7 0.85 1.05 2.5 0.85 1.8 1.6 1.7 4 2 2.1 4 4 4 1.85

31,206 12,687 6,717 4,127 101,307 9,630 3,105 109,244 5,777 3,350 35,830 77,776 27,883 15,886 1,812 17,085 120,076 3,256 140 50,424 7,643 6,105 150 3,357 9,187 5,280 1,629 732 72,146 78,926 15,320 92 256 128

LMI UMI LMI HIC HIC LMI LI LI LMI LI LMI HIC LMI UMI LMI

AFR LCR AFR OECD OECD MENA ECA AFR EAP AFR EAP LCR MENA ECA ECA

12,600,333 343,331 270,983 7,662,130 5,490,214 9,109,737 1,653,091 9,439,781 22,453,143 2,390,840 22,162 144,645 6,063,259 48,846,780 2,061,980

0.79 1.36 0.51 1.61 2.61 1.37 0.89 0.26 1.76 0.52 3.71 14.40 0.81 1.77 0.98

10,000 466 137 12,329 14,329 12,493 1,479 2,425 39,452 1,233 82 2,082 4,932 86,301 2,027

54,267,000 482,000 1,242,000 9,854,000 7,978,000 27,519,000 8,929,000 59,989,000 68,803,000 9,925,000 112,000 1,401,000 12,170,000 89,557,000 6,068,000

30,921,000 389,000 417,000 8,525,000 6,096,000 16,890,000 2,774,000 21,029,000 29,063,000 5,352,000 37,000 291000 8,909,000 67,981,000 3,485,000

1.05 1.6 0.85 1.85 2.3 1.7 1.2 0.55 1.95 0.85 3.5 10 1.15 2 1.25

32,467 622 354 15,771 14,021 28,713 3,329 11,566 56,673 4,549 130 2,910 10,245 135,962 4,356

ANNEX

83

ANNEX J (continued)


Country

Uganda United Arab Emirates United Kingdom United States Uruguay Vanuatu Venezuela, RB Vietnam Zambia Zimbabwe

Income Level LI HIC HIC HIC UMI LMI UMI LI LI LI

Region

AFR MENA OECD OECD LCR EAP LCR EAP AFR AFR

Current Available Data MSW GenTotal MSW Generation Total Urban eration Per Population Capita (kg/ (tonnes/ capita/day) day) 3,450,140 2,526,336 54,411,080 241,972,393 3,025,161 33,430 22,342,983 24,001,081 4,010,708 4,478,555

0.34 1.66 1.79 2.58 0.11 3.28 1.14 1.46 0.21 0.53

1,179 4,192 97,342 624,700 329 110 25,507 35,068 842 2,356


Total Population 54,011,000 6,268,000 65,190,000 354,930,000 3,548,000 328,000 35,373,000 106,357,000 16,539,000 15,969,000

9,713,000 5,092,000 59,738,000 305,091,000 3,333,000 113,000 34,059,000 40,505,000 6,862,000 7,539,000

0.65 2 1.85 2.3 0.6 3 1.5 1.8 0.55 0.7

Total MSW Generation (tonnes/day) 6,313 10,184 110,515 701,709 2,000 339 51,089 72,909 3,774 5,277

Summary by Income Level

Income Level

Number of Countries Included

Current Available Data


Urban MSW Generation




Total (tonnes/day)


Projected Urban MSW Generation



Total (tonnes/day)

Lower Income

38

343

0.60

204,802

1,637

676

0.86

584,272

Lower Middle Income

42

1,293

0.78

1,012,321

4,011

2,080

1.26

2,618,804

Upper Middle Income

35

572

1.16

665,586

888

619

1.59

987,039

High Income

46

774

2.13

1,649,546

1,112

912

2.06

1,879,590

Total

161

2,982

1.19

3,532,255

7,648

4,287

1.42

6,069,705

Summary by Region Current Available Data Region

Number of Countries Total Urban Included Population (millions)


Urban MSW Generation Per Capita (kg/ capita/day)

Total (tonnes/day)

Projected Population Total (millions)

Projected Urban MSW Generation

Urban (millions)

Per Capita (kg/ capita/day)

Total (tonnes/ day)

AFR

42

261

0.65

169,120

1,153

518

0.85

441,840

EAP

17

777

0.95

738,959

2,124

1,230

1.52

1,865,380

ECA

19

227

1.12

254,389

339

240

1.48

354,811

LCR

33

400

1.09

437,545

682

466

1.56

728,392

MENA

16

162

1.07

173,545

379

257

1.43

369,320

OECD

27

729

2.15

1,566,286

1,032

842

2.07

1,742,417

SAR

7

426

0.45

192,411

1,939

734

0.77

567,545

Total

161

2,982

1.19

3,532,255

7,648

4,287

1.42

6,069,705

84


ANNEX K

MSW Collection Rates by Country Country Albania Algeria Andorra Antigua and Barbuda Armenia Austria Belarus Belgium Belize Benin Brazil Bulgaria Cambodia Canada Colombia Comoros Costa Rica Croatia Cuba Czech Republic Denmark Dominica Dominican Republic Ecuador Egypt, Arab Rep. El Salvador Estonia Finland France Georgia Germany Ghana Greece Grenada Guatemala Guyana Haiti Honduras Hong Kong, China Hungary Iceland Indonesia Iraq Ireland Italy Jamaica Japan Jordan Korea, South Latvia Lebanon Luxembourg

Income

Region

Collection (%)

Urban/Total

LMI UMI HIC HIC LMI HIC UMI HIC LMI LI UMI UMI LI HIC UMI LI UMI HIC UMI HIC HIC UMI UMI LMI LMI LMI HIC HIC HIC LMI HIC LI HIC UMI LMI LMI LI LMI HIC HIC HIC LMI LMI HIC HIC UMI HIC LMI HIC UMI UMI HIC

ECA MENA OECD LCR ECA OECD ECA OECD LCR AFR LCR ECA EAP OECD LCR AFR LCR ECA LCR OECD OECD LCR LCR LCR MENA LCR ECA OECD OECD ECA OECD AFR OECD LCR LCR LCR LCR LCR EAP OECD OECD EAP MENA OECD OECD LCR OECD MENA OECD ECA MENA OECD

77 92 100 95 80 100 100 100 50 23 83 81 75 99 98 20 74 92 76 100 100 94 69 81 30-95 71 79 100 100 60 100 85 100 100 72 89 11 68 100 90 100 80 56 76 100 62 100 95+ 99 50 100 100

T U T T T T T T T T T T U T T T T T T T T T T T U T T T T T T U T T T T T T T T T U T T T T T U T T U T

ANNEX

ANNEX K (continued)

MSW Collection Rates by Country Country Macao, China Madagascar Mali Malta Marshall Islands Mauritius Mexico Monaco Morocco Nepal Netherlands Nicaragua Norway Panama Paraguay Peru Portugal Romania Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Sweden Switzerland Syrian Arab Republic Tanzania Trinidad and Tobago Tunisia Turkey Uganda United Kingdom United States Uruguay Venezuela, RB West Bank and Gaza Zambia

Income

Region

Collection (%)

Urban/Total

HIC LI LI HIC LMI UMI UMI HIC LMI LI HIC LMI HIC UMI LMI UMI HIC UMI LI UMI UMI LI HIC HIC HIC UMI UMI UMI UMI HIC HIC LMI LI HIC LMI UMI LI HIC HIC UMI UMI LMI LI

EAP AFR AFR MENA EAP AFR LCR OECD MENA SAR OECD LCR OECD LCR LCR LCR OECD ECA AFR ECA AFR AFR EAP OECD ECA LCR LCR LCR LCR OECD OECD MENA AFR LCR MENA ECA AFR OECD OECD LCR LCR MENA AFR

100 18 40 100 60 98 91 100 72-100 94 100 73 99 77 51 74 100 90 21 65 95 33-55 100 100 93 98 100 91 80 100 99 80 48 100 95 77 39 100 100 86 86 85 20

T T T T T T T T T U T T T T T T T T T T T U T T T T T T T T T U U T U T U T T T T U T

85

86


ANNEX K (continued)

MSW Collection Rates by Country Summary by Income Level Income Level

Number of Countries Included

MSW Collection (%) Lower Limit

Upper Limit

Lower Income

13

10.62

55.00

Lower Middle Income

20

50.20

95+

Upper Middle Income

27

50.00

100.00

High Income

35

76.00

100.00

Total

95

Summary by Region Number of Countries Included

Lower Limit

Upper Limit

AFR

12

17.70

55.00

EAP

6

60.00

100.00

ECA

12

50.00

100.00

LCR

28

10.62

100.00

MENA

10

55.60

95+

OECD

26

76.00

100.00

SAR

1

Total

95

Region

MSW Collection (%)

94.00

ANNEX

ANNEX L

MSW Disposal Methods by Country Country

Income

Region

Algeria Antigua and Barbuda Armenia Australia Austria Belarus Belgium Belize Bulgaria Cambodia Cameroon Canada Chile Colombia Costa Rica Croatia Cuba Cyprus Czech Republic Denmark Dominica Greece Grenada Guatemala Guyana Haiti Hong Kong, China Hungary Iceland2 Ireland Israel Italy Jamaica Japan Jordan3 Korea, South Kyrgyz Republic Latvia Lebanon Lithuania Luxembourg Macao, China2 Madagascar2 Malta Marshall Islands Mauritius Mexico Monaco4 Morocco Netherlands New Zealand Nicaragua

UMI

MNA

HIC

LCR

LMI HIC HIC UMI HIC LMI UMI LI LMI HIC UMI UMI UMI HIC UMI HIC HIC HIC UMI HIC UMI LMI LMI LI HIC HIC HIC HIC HIC HIC UMI HIC LMI HIC LI UMI UMI UMI HIC HIC LI HIC LMI UMI UMI HIC LMI HIC HIC LMI

ECA OECD OECD ECA OECD LCR ECA EAP AFR OECD LCR LCR LCR ECA LCR ECA OECD OECD LCR OECD LCR LCR LCR LCR EAP OECD OECD OECD MENA OECD LCR OECD MENA OECD ECA ECA MENA ECA OECD EAP AFR MENA EAP AFR LCR OECD MENA OECD OECD LCR

Dumps (%) 96.80

Landfills (%) 0.20

Compost (%) 1.00

99.00 — — — — — — — 100.00 95.00 — — 54.00 22.37 — — — — — — — — — 37 24 — — — — — — — — — — — 60 37 — — — — — — — — — 95 — — 34

100.00 69.66 6.75 96.00 11.57 100.00 82.90 — — — 100.00 46.00 71.95 69.50 100.00 87.20 79.78 5.09 100.00 92 90 22 59 — 55 90 72 66 90 54 100 3 85 36 100 40 46 44 19 21 97 88 — 91 97 27 1 2 85 28

— — 44.72 4.00 22.77 — — — — 12.48 — — — 0.90 11.10 — 3.24 15.28 — — — — — — — 1 9 — — 33 — — — — — — 8 — 19 — 4 — 6 — — — — 23 — —

Recycled (%)

WTE (%)

Other (%)

2.00

—

—

1.00

—

—

— 30.34 26.54 — 31.10 — — — 5.00 26.78 — — 0.29 2.40 4.80 — 1.27 25.57 — 8 — — — — 45 3 16 34 10 — — 17 — 49 — — 8 4 23 — — — 31 2 3 4 4 25 15 —

— — 21.10 — 34.32 — — — — — — — — — — — 13.97 54.04 — — — — — — — 6 9 — — 12 — 74 — 14 — — — 2 39 — — — — — — — — 32 — —

— — 0.90 — — — 17.10 — — 60.74 — — 5.39 27.20 — 12.80 1.74 0.03 — — 10 78 4 76 — 0 — — — — — 6 15 — — — 1 50 — 100 — 13 63 — — 132 — 17 — 38

87

88


ANNEX L (continued)

MSW Disposal Methods by Country Dumps (%)

Landfills (%)

Compost (%)

Recycled (%)

Country

Income

Region

WTE (%)

Other (%)

Niger Norway Panama Paraguay Peru Poland Portugal5 Romania Singapore6 Slovak Republic Slovenia Spain St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Sweden Switzerland Syrian Arab Republic Thailand Trinidad and Tobago Tunisia Turkey Uganda United Kingdom United States Uruguay Venezuela, RB West Bank and Gaza

LI HIC UMI LMI UMI UMI HIC UMI HIC HIC HIC HIC UMI

AFR OECD LCR LCR LCR ECA OECD ECA EAP OECD ECA OECD LCR

— — 20 42 19 — — — — — — — —

64 26 56 44 66 92 64 75 15 78 86 52 100

— 15 — — — 3 6 — — 1 — 33 —

4 34 — — — 4 9 — 47 1 — 9 —

— 25 — — — 0 21 — — 12 — 7 —

32 0 24 14 15 — — 25 49 7 14 — —

UMI UMI

LCR LCR

— —

70 78

— —

— —

— —

30 22

UMI HIC HIC

LCR OECD OECD

100 — —

— 5 1

— 10 16

— 34 34

— 50 50

0 1 —

LMI

MENA

>60